import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
pd.set_option('display.max_rows', None)
from plotly.subplots import make_subplots
import seaborn as sns
import datetime
#Plotly Libraris
import plotly.express as px
# Minmax scaler
from sklearn.preprocessing import MinMaxScaler
#itertools
import itertools
#dataframe display settings
pd.set_option('display.max_columns', 5000000)
pd.set_option('display.max_rows', 50000000)
#to suppress un-necessary warnings
import warnings
warnings.filterwarnings('ignore')
#Package to flatten python lists
from pandas.core.common import flatten
covid_data = pd.read_csv('covid_19_data.csv')
covid_confirmed = pd.read_csv('time_series_covid_19_confirmed.csv')
covid_deaths = pd.read_csv('time_series_covid_19_deaths.csv')
covid_recovered = pd.read_csv('time_series_covid_19_recovered.csv')
covid_confirmed.describe()
| Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | 4/8/20 | 4/9/20 | 4/10/20 | 4/11/20 | 4/12/20 | 4/13/20 | 4/14/20 | 4/15/20 | 4/16/20 | 4/17/20 | 4/18/20 | 4/19/20 | 4/20/20 | 4/21/20 | 4/22/20 | 4/23/20 | 4/24/20 | 4/25/20 | 4/26/20 | 4/27/20 | 4/28/20 | 4/29/20 | 4/30/20 | 5/1/20 | 5/2/20 | 5/3/20 | 5/4/20 | 5/5/20 | 5/6/20 | 5/7/20 | 5/8/20 | 5/9/20 | 5/10/20 | 5/11/20 | 5/12/20 | 5/13/20 | 5/14/20 | 5/15/20 | 5/16/20 | 5/17/20 | 5/18/20 | 5/19/20 | 5/20/20 | 5/21/20 | 5/22/20 | 5/23/20 | 5/24/20 | 5/25/20 | 5/26/20 | 5/27/20 | 5/28/20 | 5/29/20 | 5/30/20 | 5/31/20 | 6/1/20 | 6/2/20 | 6/3/20 | 6/4/20 | 6/5/20 | 6/6/20 | 6/7/20 | 6/8/20 | 6/9/20 | 6/10/20 | 6/11/20 | 6/12/20 | 6/13/20 | 6/14/20 | 6/15/20 | 6/16/20 | 6/17/20 | 6/18/20 | 6/19/20 | 6/20/20 | 6/21/20 | 6/22/20 | 6/23/20 | 6/24/20 | 6/25/20 | 6/26/20 | 6/27/20 | 6/28/20 | 6/29/20 | 6/30/20 | 7/1/20 | 7/2/20 | 7/3/20 | 7/4/20 | 7/5/20 | 7/6/20 | 7/7/20 | 7/8/20 | 7/9/20 | 7/10/20 | 7/11/20 | 7/12/20 | 7/13/20 | 7/14/20 | 7/15/20 | 7/16/20 | 7/17/20 | 7/18/20 | 7/19/20 | 7/20/20 | 7/21/20 | 7/22/20 | 7/23/20 | 7/24/20 | 7/25/20 | 7/26/20 | 7/27/20 | 7/28/20 | 7/29/20 | 7/30/20 | 7/31/20 | 8/1/20 | 8/2/20 | 8/3/20 | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | 8/14/20 | 8/15/20 | 8/16/20 | 8/17/20 | 8/18/20 | 8/19/20 | 8/20/20 | 8/21/20 | 8/22/20 | 8/23/20 | 8/24/20 | 8/25/20 | 8/26/20 | 8/27/20 | 8/28/20 | 8/29/20 | 8/30/20 | 8/31/20 | 9/1/20 | 9/2/20 | 9/3/20 | 9/4/20 | 9/5/20 | 9/6/20 | 9/7/20 | 9/8/20 | 9/9/20 | 9/10/20 | 9/11/20 | 9/12/20 | 9/13/20 | 9/14/20 | 9/15/20 | 9/16/20 | 9/17/20 | 9/18/20 | 9/19/20 | 9/20/20 | 9/21/20 | 9/22/20 | 9/23/20 | 9/24/20 | 9/25/20 | 9/26/20 | 9/27/20 | 9/28/20 | 9/29/20 | 9/30/20 | 10/1/20 | 10/2/20 | 10/3/20 | 10/4/20 | 10/5/20 | 10/6/20 | 10/7/20 | 10/8/20 | 10/9/20 | 10/10/20 | 10/11/20 | 10/12/20 | 10/13/20 | 10/14/20 | 10/15/20 | 10/16/20 | 10/17/20 | 10/18/20 | 10/19/20 | 10/20/20 | 10/21/20 | 10/22/20 | 10/23/20 | 10/24/20 | 10/25/20 | 10/26/20 | 10/27/20 | 10/28/20 | 10/29/20 | 10/30/20 | 10/31/20 | 11/1/20 | 11/2/20 | 11/3/20 | 11/4/20 | 11/5/20 | 11/6/20 | 11/7/20 | 11/8/20 | 11/9/20 | 11/10/20 | 11/11/20 | 11/12/20 | 11/13/20 | 11/14/20 | 11/15/20 | 11/16/20 | 11/17/20 | 11/18/20 | 11/19/20 | 11/20/20 | 11/21/20 | 11/22/20 | 11/23/20 | 11/24/20 | 11/25/20 | 11/26/20 | 11/27/20 | 11/28/20 | 11/29/20 | 11/30/20 | 12/1/20 | 12/2/20 | 12/3/20 | 12/4/20 | 12/5/20 | 12/6/20 | 12/7/20 | 12/8/20 | 12/9/20 | 12/10/20 | 12/11/20 | 12/12/20 | 12/13/20 | 12/14/20 | 12/15/20 | 12/16/20 | 12/17/20 | 12/18/20 | 12/19/20 | 12/20/20 | 12/21/20 | 12/22/20 | 12/23/20 | 12/24/20 | 12/25/20 | 12/26/20 | 12/27/20 | 12/28/20 | 12/29/20 | 12/30/20 | 12/31/20 | 1/1/21 | 1/2/21 | 1/3/21 | 1/4/21 | 1/5/21 | 1/6/21 | 1/7/21 | 1/8/21 | 1/9/21 | 1/10/21 | 1/11/21 | 1/12/21 | 1/13/21 | 1/14/21 | 1/15/21 | 1/16/21 | 1/17/21 | 1/18/21 | 1/19/21 | 1/20/21 | 1/21/21 | 1/22/21 | 1/23/21 | 1/24/21 | 1/25/21 | 1/26/21 | 1/27/21 | 1/28/21 | 1/29/21 | 1/30/21 | 1/31/21 | 2/1/21 | 2/2/21 | 2/3/21 | 2/4/21 | 2/5/21 | 2/6/21 | 2/7/21 | 2/8/21 | 2/9/21 | 2/10/21 | 2/11/21 | 2/12/21 | 2/13/21 | 2/14/21 | 2/15/21 | 2/16/21 | 2/17/21 | 2/18/21 | 2/19/21 | 2/20/21 | 2/21/21 | 2/22/21 | 2/23/21 | 2/24/21 | 2/25/21 | 2/26/21 | 2/27/21 | 2/28/21 | 3/1/21 | 3/2/21 | 3/3/21 | 3/4/21 | 3/5/21 | 3/6/21 | 3/7/21 | 3/8/21 | 3/9/21 | 3/10/21 | 3/11/21 | 3/12/21 | 3/13/21 | 3/14/21 | 3/15/21 | 3/16/21 | 3/17/21 | 3/18/21 | 3/19/21 | 3/20/21 | 3/21/21 | 3/22/21 | 3/23/21 | 3/24/21 | 3/25/21 | 3/26/21 | 3/27/21 | 3/28/21 | 3/29/21 | 3/30/21 | 3/31/21 | 4/1/21 | 4/2/21 | 4/3/21 | 4/4/21 | 4/5/21 | 4/6/21 | 4/7/21 | 4/8/21 | 4/9/21 | 4/10/21 | 4/11/21 | 4/12/21 | 4/13/21 | 4/14/21 | 4/15/21 | 4/16/21 | 4/17/21 | 4/18/21 | 4/19/21 | 4/20/21 | 4/21/21 | 4/22/21 | 4/23/21 | 4/24/21 | 4/25/21 | 4/26/21 | 4/27/21 | 4/28/21 | 4/29/21 | 4/30/21 | 5/1/21 | 5/2/21 | 5/3/21 | 5/4/21 | 5/5/21 | 5/6/21 | 5/7/21 | 5/8/21 | 5/9/21 | 5/10/21 | 5/11/21 | 5/12/21 | 5/13/21 | 5/14/21 | 5/15/21 | 5/16/21 | 5/17/21 | 5/18/21 | 5/19/21 | 5/20/21 | 5/21/21 | 5/22/21 | 5/23/21 | 5/24/21 | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 274.000000 | 274.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 276.000000 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 | 2.760000e+02 |
| mean | 20.447559 | 22.328281 | 2.018116 | 2.373188 | 3.409420 | 5.192029 | 7.673913 | 10.605072 | 20.210145 | 22.344203 | 29.836957 | 35.967391 | 43.615942 | 60.822464 | 72.054348 | 86.586957 | 100.155797 | 111.605072 | 124.623188 | 134.528986 | 145.507246 | 154.960145 | 162.358696 | 163.873188 | 218.775362 | 242.423913 | 250.188406 | 258.097826 | 265.471014 | 272.289855 | 274.101449 | 276.130435 | 278.409420 | 284.789855 | 286.166667 | 288.210145 | 291.300725 | 294.840580 | 299.768116 | 304.789855 | 311.641304 | 320.268116 | 327.452899 | 336.851449 | 345.213768 | 355.398551 | 369.576087 | 384.413043 | 398.721014 | 413.836957 | 431.307971 | 459.065217 | 480.043478 | 532.159420 | 572.282609 | 613.112319 | 666.764493 | 724.528986 | 795.416667 | 893.420290 | 1004.923913 | 1120.833333 | 1247.119565 | 1402.318841 | 1551.518116 | 1736.304348 | 1964.119565 | 2199.775362 | 2443.543478 | 2658.170290 | 2894.742754 | 3173.166667 | 3473.195652 | 3774.137681 | 4076.887681 | 4288.347826 | 4551.362319 | 4816.416667 | 5069.427536 | 5371.521739 | 5686.789855 | 5996.695652 | 6265.836957 | 6699.518116 | 6955.829710 | 7260.217391 | 7539.615942 | 7885.742754 | 8204.572464 | 8484.427536 | 8761.981884 | 9037.409420 | 9311.862319 | 9606.235507 | 9911.485507 | 10216.181159 | 10514.221014 | 10773.597826 | 1.102792e+04 | 1.130161e+04 | 1.158100e+04 | 1.188351e+04 | 1.220372e+04 | 1.249067e+04 | 1.276739e+04 | 1.304887e+04 | 1.334054e+04 | 1.366673e+04 | 1.398813e+04 | 1.431523e+04 | 1.462401e+04 | 1.489566e+04 | 1.517331e+04 | 1.547943e+04 | 1.578733e+04 | 1.613600e+04 | 1.648285e+04 | 1.682502e+04 | 1.710771e+04 | 1.743078e+04 | 1.778141e+04 | 1.815054e+04 | 1.853693e+04 | 1.892251e+04 | 1.930224e+04 | 1.964358e+04 | 1.995778e+04 | 2.029824e+04 | 2.067056e+04 | 2.110275e+04 | 2.154080e+04 | 2.203542e+04 | 2.242096e+04 | 2.276876e+04 | 2.321066e+04 | 2.362461e+04 | 2.409904e+04 | 2.457449e+04 | 2.505904e+04 | 2.546616e+04 | 2.583752e+04 | 2.629064e+04 | 2.678015e+04 | 2.728022e+04 | 2.774676e+04 | 2.823694e+04 | 2.871623e+04 | 2.914977e+04 | 2.966457e+04 | 3.018539e+04 | 3.069649e+04 | 3.134835e+04 | 3.191882e+04 | 3.238150e+04 | 3.288643e+04 | 3.349082e+04 | 3.411616e+04 | 3.476246e+04 | 3.545666e+04 | 3.610206e+04 | 3.669580e+04 | 3.725865e+04 | 3.789105e+04 | 3.867948e+04 | 3.944015e+04 | 4.016980e+04 | 4.087201e+04 | 4.153869e+04 | 4.213280e+04 | 4.289861e+04 | 4.367454e+04 | 4.449638e+04 | 4.533940e+04 | 4.612376e+04 | 4.682040e+04 | 4.751404e+04 | 4.831907e+04 | 4.915772e+04 | 5.006827e+04 | 5.094777e+04 | 5.180349e+04 | 5.257680e+04 | 5.332722e+04 | 5.417389e+04 | 5.518791e+04 | 5.621274e+04 | 5.723112e+04 | 5.815161e+04 | 5.892348e+04 | 5.974424e+04 | 6.066013e+04 | 6.171711e+04 | 6.273197e+04 | 6.378539e+04 | 6.468516e+04 | 6.552209e+04 | 6.627019e+04 | 6.720476e+04 | 6.820684e+04 | 6.924018e+04 | 7.026279e+04 | 7.119945e+04 | 7.201352e+04 | 7.283246e+04 | 7.376569e+04 | 7.476803e+04 | 7.581592e+04 | 7.691645e+04 | 7.781395e+04 | 7.859101e+04 | 7.934366e+04 | 8.027937e+04 | 8.129238e+04 | 8.227989e+04 | 8.322629e+04 | 8.418372e+04 | 8.493064e+04 | 8.574683e+04 | 8.663089e+04 | 8.765912e+04 | 8.868773e+04 | 8.971536e+04 | 9.067113e+04 | 9.147173e+04 | 9.242424e+04 | 9.338411e+04 | 9.441130e+04 | 9.543093e+04 | 9.656867e+04 | 9.754690e+04 | 9.838192e+04 | 9.917134e+04 | 1.000489e+05 | 1.010808e+05 | 1.021673e+05 | 1.033317e+05 | 1.043701e+05 | 1.052495e+05 | 1.062051e+05 | 1.072394e+05 | 1.083428e+05 | 1.094837e+05 | 1.106645e+05 | 1.117229e+05 | 1.126364e+05 | 1.136367e+05 | 1.146676e+05 | 1.156679e+05 | 1.169470e+05 | 1.181458e+05 | 1.191909e+05 | 1.201017e+05 | 1.210248e+05 | 1.220490e+05 | 1.232309e+05 | 1.243842e+05 | 1.254634e+05 | 1.266665e+05 | 1.276136e+05 | 1.287042e+05 | 1.298873e+05 | 1.311590e+05 | 1.324682e+05 | 1.337758e+05 | 1.350696e+05 | 1.361091e+05 | 1.371663e+05 | 1.383230e+05 | 1.397083e+05 | 1.411828e+05 | 1.426748e+05 | 1.440250e+05 | 1.451719e+05 | 1.465749e+05 | 1.479843e+05 | 1.495962e+05 | 1.513090e+05 | 1.531090e+05 | 1.547640e+05 | 1.560707e+05 | 1.578330e+05 | 1.595368e+05 | 1.613914e+05 | 1.633857e+05 | 1.654547e+05 | 1.671821e+05 | 1.688574e+05 | 1.708977e+05 | 1.729040e+05 | 1.747575e+05 | 1.769189e+05 | 1.792554e+05 | 1.814180e+05 | 1.831715e+05 | 1.849887e+05 | 1.870154e+05 | 1.893654e+05 | 1.917189e+05 | 1.940814e+05 | 1.962371e+05 | 1.979543e+05 | 1.998907e+05 | 2.021053e+05 | 2.043774e+05 | 2.067455e+05 | 2.091685e+05 | 2.112973e+05 | 2.130654e+05 | 2.149687e+05 | 2.171073e+05 | 2.194125e+05 | 2.215229e+05 | 2.240126e+05 | 2.261359e+05 | 2.279061e+05 | 2.297490e+05 | 2.319667e+05 | 2.343269e+05 | 2.368426e+05 | 2.393182e+05 | 2.416449e+05 | 2.435969e+05 | 2.454810e+05 | 2.478126e+05 | 2.502399e+05 | 2.556682e+05 | 2.582207e+05 | 2.605209e+05 | 2.624442e+05 | 2.643478e+05 | 2.666529e+05 | 2.693113e+05 | 2.719909e+05 | 2.746013e+05 | 2.768160e+05 | 2.787489e+05 | 2.807391e+05 | 2.830951e+05 | 2.856136e+05 | 2.880290e+05 | 2.897158e+05 | 2.915730e+05 | 2.931616e+05 | 2.949599e+05 | 2.973673e+05 | 3.001253e+05 | 3.027522e+05 | 3.046834e+05 | 3.069528e+05 | 3.088832e+05 | 3.108909e+05 | 3.135725e+05 | 3.164108e+05 | 3.196023e+05 | 3.225125e+05 | 3.252697e+05 | 3.274042e+05 | 3.296504e+05 | 3.322066e+05 | 3.349181e+05 | 3.376544e+05 | 3.404341e+05 | 3.427498e+05 | 3.446673e+05 | 3.465335e+05 | 3.487379e+05 | 3.512485e+05 | 3.536265e+05 | 3.560144e+05 | 3.580729e+05 | 3.596875e+05 | 3.614895e+05 | 3.635049e+05 | 3.656728e+05 | 3.678984e+05 | 3.700377e+05 | 3.719048e+05 | 3.732875e+05 | 3.749063e+05 | 3.765656e+05 | 3.784568e+05 | 3.801485e+05 | 3.820884e+05 | 3.834367e+05 | 3.848790e+05 | 3.860236e+05 | 3.875744e+05 | 3.891532e+05 | 3.907527e+05 | 3.923059e+05 | 3.936585e+05 | 3.947231e+05 | 3.957491e+05 | 3.970209e+05 | 3.984541e+05 | 3.999161e+05 | 4.014107e+05 | 4.027530e+05 | 4.038923e+05 | 4.049373e+05 | 4.063462e+05 | 4.079583e+05 | 4.095796e+05 | 4.111848e+05 | 4.125951e+05 | 4.136947e+05 | 4.148029e+05 | 4.159272e+05 | 4.175255e+05 | 4.191670e+05 | 4.207867e+05 | 4.222742e+05 | 4.236086e+05 | 4.246910e+05 | 4.261934e+05 | 4.278857e+05 | 4.296136e+05 | 4.313865e+05 | 4.330319e+05 | 4.343376e+05 | 4.356017e+05 | 4.373156e+05 | 4.392695e+05 | 4.412757e+05 | 4.433108e+05 | 4.451181e+05 | 4.466519e+05 | 4.481621e+05 | 4.500253e+05 | 4.523209e+05 | 4.546808e+05 | 4.570046e+05 | 4.591227e+05 | 4.608254e+05 | 4.624863e+05 | 4.645453e+05 | 4.670206e+05 | 4.695996e+05 | 4.718957e+05 | 4.738140e+05 | 4.758169e+05 | 4.775908e+05 | 4.797795e+05 | 4.822590e+05 | 4.853019e+05 | 4.880122e+05 | 4.904199e+05 | 4.929226e+05 | 4.951567e+05 | 4.979812e+05 | 5.009415e+05 | 5.039007e+05 | 5.069980e+05 | 5.098533e+05 | 5.123369e+05 | 5.148493e+05 | 5.179451e+05 | 5.211696e+05 | 5.244296e+05 | 5.277041e+05 | 5.306785e+05 | 5.332957e+05 | 5.357696e+05 | 5.388292e+05 | 5.421118e+05 | 5.453640e+05 | 5.485488e+05 | 5.514354e+05 | 5.538786e+05 | 5.563482e+05 | 5.592737e+05 | 5.623216e+05 | 5.654737e+05 | 5.684865e+05 | 5.713364e+05 | 5.736608e+05 | 5.759141e+05 | 5.785898e+05 | 5.813437e+05 | 5.839716e+05 | 5.865732e+05 | 5.888468e+05 | 5.908331e+05 | 5.927885e+05 | 5.950428e+05 | 5.974743e+05 | 5.984867e+05 | 6.007543e+05 | 6.028478e+05 | 6.045727e+05 | 6.062187e+05 | 6.081457e+05 | 6.102044e+05 | 6.122130e+05 | 6.140244e+05 | 6.157665e+05 |
| std | 25.189838 | 74.369096 | 26.781738 | 26.879567 | 33.464159 | 46.575328 | 65.089830 | 87.699030 | 215.201418 | 216.521511 | 298.377294 | 353.754834 | 435.313401 | 676.494833 | 817.788348 | 1007.884613 | 1187.714772 | 1335.148694 | 1506.165586 | 1635.472132 | 1787.715018 | 1913.867390 | 2012.441881 | 2012.780205 | 2904.064734 | 3276.799205 | 3387.689187 | 3503.968094 | 3612.651852 | 3714.479264 | 3735.499036 | 3760.204488 | 3773.510303 | 3859.001700 | 3859.042744 | 3871.327211 | 3901.385100 | 3925.695938 | 3950.861847 | 3970.967454 | 3998.322007 | 4034.863650 | 4049.462611 | 4061.612746 | 4072.844072 | 4086.702355 | 4103.621569 | 4120.687415 | 4136.850716 | 4156.134361 | 4171.787491 | 4204.715289 | 4245.631689 | 4307.641449 | 4388.697303 | 4481.847988 | 4595.234467 | 4729.881563 | 4917.529380 | 5198.458483 | 5558.888496 | 5978.468064 | 6438.583287 | 7059.158412 | 7692.047092 | 8515.267351 | 9616.751990 | 10796.263981 | 12051.896248 | 13215.677758 | 14560.609057 | 16156.590130 | 18061.272464 | 19980.433792 | 21937.530422 | 23713.968743 | 25484.640357 | 27337.924989 | 29153.805488 | 31090.393817 | 33251.336708 | 35320.515033 | 37102.821892 | 39122.328963 | 40755.971809 | 42610.818905 | 44219.082428 | 46214.848580 | 48245.258412 | 49937.040485 | 51577.333719 | 53362.953059 | 54978.000435 | 56764.926036 | 58776.526398 | 60596.489622 | 62441.847303 | 64044.205417 | 6.552348e+04 | 6.705787e+04 | 6.868196e+04 | 7.047023e+04 | 7.257770e+04 | 7.425877e+04 | 7.576732e+04 | 7.727575e+04 | 7.882996e+04 | 8.044587e+04 | 8.219595e+04 | 8.392131e+04 | 8.552868e+04 | 8.677601e+04 | 8.808444e+04 | 8.959719e+04 | 9.098943e+04 | 9.274807e+04 | 9.443268e+04 | 9.604954e+04 | 9.729713e+04 | 9.882773e+04 | 1.003336e+05 | 1.019597e+05 | 1.037555e+05 | 1.054745e+05 | 1.070214e+05 | 1.084838e+05 | 1.098285e+05 | 1.112987e+05 | 1.127695e+05 | 1.145643e+05 | 1.164829e+05 | 1.184969e+05 | 1.200091e+05 | 1.213484e+05 | 1.231970e+05 | 1.249315e+05 | 1.268717e+05 | 1.290061e+05 | 1.309020e+05 | 1.324654e+05 | 1.339321e+05 | 1.357696e+05 | 1.377975e+05 | 1.399117e+05 | 1.420105e+05 | 1.440898e+05 | 1.457848e+05 | 1.475301e+05 | 1.498043e+05 | 1.522021e+05 | 1.544950e+05 | 1.576902e+05 | 1.604728e+05 | 1.624551e+05 | 1.649534e+05 | 1.681080e+05 | 1.713030e+05 | 1.746614e+05 | 1.785074e+05 | 1.819383e+05 | 1.851200e+05 | 1.881551e+05 | 1.917104e+05 | 1.958767e+05 | 2.003543e+05 | 2.044396e+05 | 2.081761e+05 | 2.118384e+05 | 2.149400e+05 | 2.195864e+05 | 2.242212e+05 | 2.289631e+05 | 2.340540e+05 | 2.386051e+05 | 2.426892e+05 | 2.466804e+05 | 2.517206e+05 | 2.567652e+05 | 2.623765e+05 | 2.675391e+05 | 2.721045e+05 | 2.764603e+05 | 2.807680e+05 | 2.857579e+05 | 2.919120e+05 | 2.978339e+05 | 3.038883e+05 | 3.093737e+05 | 3.136581e+05 | 3.179850e+05 | 3.233479e+05 | 3.297459e+05 | 3.356704e+05 | 3.416895e+05 | 3.467784e+05 | 3.508543e+05 | 3.546159e+05 | 3.599847e+05 | 3.654148e+05 | 3.711076e+05 | 3.767052e+05 | 3.820745e+05 | 3.863059e+05 | 3.904291e+05 | 3.955635e+05 | 4.012727e+05 | 4.069172e+05 | 4.129951e+05 | 4.178773e+05 | 4.217805e+05 | 4.254278e+05 | 4.304671e+05 | 4.358100e+05 | 4.408983e+05 | 4.458025e+05 | 4.509908e+05 | 4.548297e+05 | 4.586504e+05 | 4.633674e+05 | 4.690260e+05 | 4.744405e+05 | 4.798953e+05 | 4.851529e+05 | 4.893378e+05 | 4.942431e+05 | 4.995404e+05 | 5.050958e+05 | 5.106894e+05 | 5.168900e+05 | 5.223175e+05 | 5.267826e+05 | 5.304590e+05 | 5.347905e+05 | 5.402152e+05 | 5.459276e+05 | 5.522793e+05 | 5.579486e+05 | 5.627579e+05 | 5.674234e+05 | 5.730499e+05 | 5.789388e+05 | 5.850446e+05 | 5.913477e+05 | 5.971250e+05 | 6.021130e+05 | 6.072793e+05 | 6.127128e+05 | 6.173920e+05 | 6.241520e+05 | 6.300694e+05 | 6.357665e+05 | 6.406019e+05 | 6.449462e+05 | 6.504437e+05 | 6.561375e+05 | 6.619373e+05 | 6.671169e+05 | 6.734340e+05 | 6.778696e+05 | 6.822601e+05 | 6.879220e+05 | 6.938053e+05 | 6.996221e+05 | 7.054512e+05 | 7.112293e+05 | 7.159710e+05 | 7.201323e+05 | 7.249918e+05 | 7.307852e+05 | 7.367232e+05 | 7.428319e+05 | 7.482336e+05 | 7.525239e+05 | 7.579644e+05 | 7.633378e+05 | 7.689482e+05 | 7.750803e+05 | 7.815337e+05 | 7.877646e+05 | 7.925100e+05 | 7.977152e+05 | 8.036316e+05 | 8.098824e+05 | 8.165989e+05 | 8.235833e+05 | 8.298689e+05 | 8.365226e+05 | 8.427055e+05 | 8.505891e+05 | 8.574585e+05 | 8.652819e+05 | 8.741399e+05 | 8.824588e+05 | 8.896733e+05 | 8.969607e+05 | 9.054399e+05 | 9.151440e+05 | 9.251464e+05 | 9.356473e+05 | 9.456560e+05 | 9.533872e+05 | 9.622528e+05 | 9.721320e+05 | 9.825320e+05 | 9.937887e+05 | 1.005449e+06 | 1.016019e+06 | 1.024761e+06 | 1.034521e+06 | 1.044877e+06 | 1.056038e+06 | 1.063871e+06 | 1.075808e+06 | 1.085719e+06 | 1.094187e+06 | 1.103312e+06 | 1.114502e+06 | 1.126379e+06 | 1.139309e+06 | 1.152575e+06 | 1.164990e+06 | 1.175349e+06 | 1.185998e+06 | 1.198865e+06 | 1.211780e+06 | 1.227725e+06 | 1.241604e+06 | 1.254199e+06 | 1.264821e+06 | 1.275708e+06 | 1.287871e+06 | 1.302383e+06 | 1.316620e+06 | 1.331139e+06 | 1.342735e+06 | 1.353551e+06 | 1.364819e+06 | 1.376853e+06 | 1.390331e+06 | 1.402334e+06 | 1.408910e+06 | 1.421266e+06 | 1.430285e+06 | 1.440305e+06 | 1.452540e+06 | 1.466628e+06 | 1.480273e+06 | 1.489627e+06 | 1.506281e+06 | 1.518026e+06 | 1.528745e+06 | 1.542877e+06 | 1.558233e+06 | 1.575265e+06 | 1.592018e+06 | 1.607969e+06 | 1.620355e+06 | 1.632822e+06 | 1.646672e+06 | 1.660797e+06 | 1.675286e+06 | 1.690198e+06 | 1.702658e+06 | 1.713274e+06 | 1.722121e+06 | 1.733346e+06 | 1.745182e+06 | 1.757339e+06 | 1.769350e+06 | 1.780189e+06 | 1.788294e+06 | 1.797563e+06 | 1.807221e+06 | 1.817334e+06 | 1.828301e+06 | 1.838997e+06 | 1.848240e+06 | 1.855209e+06 | 1.863516e+06 | 1.871258e+06 | 1.879529e+06 | 1.887725e+06 | 1.896641e+06 | 1.902796e+06 | 1.909487e+06 | 1.914878e+06 | 1.921875e+06 | 1.928694e+06 | 1.935891e+06 | 1.942765e+06 | 1.948775e+06 | 1.953208e+06 | 1.957202e+06 | 1.962125e+06 | 1.967577e+06 | 1.972956e+06 | 1.978870e+06 | 1.984308e+06 | 1.988507e+06 | 1.992520e+06 | 1.998123e+06 | 2.004128e+06 | 2.010243e+06 | 2.016319e+06 | 2.021606e+06 | 2.025600e+06 | 2.029872e+06 | 2.034331e+06 | 2.040029e+06 | 2.045853e+06 | 2.051615e+06 | 2.056814e+06 | 2.061278e+06 | 2.064880e+06 | 2.070051e+06 | 2.075641e+06 | 2.081359e+06 | 2.087284e+06 | 2.092601e+06 | 2.096463e+06 | 2.100945e+06 | 2.106530e+06 | 2.112749e+06 | 2.119128e+06 | 2.125602e+06 | 2.131567e+06 | 2.135776e+06 | 2.140756e+06 | 2.146678e+06 | 2.154903e+06 | 2.162479e+06 | 2.170245e+06 | 2.177264e+06 | 2.182402e+06 | 2.188379e+06 | 2.195033e+06 | 2.202821e+06 | 2.211360e+06 | 2.219049e+06 | 2.225603e+06 | 2.231218e+06 | 2.238234e+06 | 2.246004e+06 | 2.254999e+06 | 2.265021e+06 | 2.274874e+06 | 2.283593e+06 | 2.291666e+06 | 2.300085e+06 | 2.310639e+06 | 2.321402e+06 | 2.332431e+06 | 2.344444e+06 | 2.355267e+06 | 2.365167e+06 | 2.375773e+06 | 2.388134e+06 | 2.401311e+06 | 2.414529e+06 | 2.428350e+06 | 2.441772e+06 | 2.453494e+06 | 2.465097e+06 | 2.479111e+06 | 2.494151e+06 | 2.509368e+06 | 2.525012e+06 | 2.539842e+06 | 2.552421e+06 | 2.565681e+06 | 2.580650e+06 | 2.596690e+06 | 2.613039e+06 | 2.629200e+06 | 2.644631e+06 | 2.657808e+06 | 2.670406e+06 | 2.684667e+06 | 2.699614e+06 | 2.714109e+06 | 2.728461e+06 | 2.741432e+06 | 2.752445e+06 | 2.763204e+06 | 2.775108e+06 | 2.787589e+06 | 2.797198e+06 | 2.808982e+06 | 2.819859e+06 | 2.828976e+06 | 2.837786e+06 | 2.847660e+06 | 2.857958e+06 | 2.867378e+06 | 2.875765e+06 | 2.883996e+06 |
| min | -51.796300 | -178.116500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 25% | 4.933349 | -22.036550 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 3.000000 | 3.000000 | 3.750000 | 5.000000 | 6.000000 | 7.750000 | 8.000000 | 9.000000 | 10.000000 | 11.750000 | 13.000000 | 13.750000 | 15.000000 | 16.000000 | 17.500000 | 17.500000 | 18.000000 | 19.000000 | 19.000000 | 19.000000 | 20.750000 | 21.000000 | 23.000000 | 28.750000 | 31.500000 | 31.750000 | 33.500000 | 35.000000 | 38.500000 | 39.000000 | 42.750000 | 44.250000 | 45.750000 | 48.000000 | 51.250000 | 52.750000 | 5.575000e+01 | 5.700000e+01 | 5.775000e+01 | 6.025000e+01 | 6.975000e+01 | 7.350000e+01 | 7.475000e+01 | 7.500000e+01 | 7.750000e+01 | 7.750000e+01 | 7.900000e+01 | 8.175000e+01 | 8.175000e+01 | 8.175000e+01 | 8.350000e+01 | 8.425000e+01 | 8.425000e+01 | 9.100000e+01 | 9.550000e+01 | 9.550000e+01 | 9.550000e+01 | 9.550000e+01 | 1.055000e+02 | 1.055000e+02 | 1.055000e+02 | 1.055000e+02 | 1.055000e+02 | 1.055000e+02 | 1.067500e+02 | 1.137500e+02 | 1.197500e+02 | 1.235000e+02 | 1.235000e+02 | 1.280000e+02 | 1.302500e+02 | 1.302500e+02 | 1.327500e+02 | 1.342500e+02 | 1.350000e+02 | 1.350000e+02 | 1.382500e+02 | 1.405000e+02 | 1.410000e+02 | 1.410000e+02 | 1.432500e+02 | 1.455000e+02 | 1.455000e+02 | 1.457500e+02 | 1.457500e+02 | 1.462500e+02 | 1.487500e+02 | 1.515000e+02 | 1.525000e+02 | 1.525000e+02 | 1.525000e+02 | 1.532500e+02 | 1.547500e+02 | 1.547500e+02 | 1.547500e+02 | 1.547500e+02 | 1.602500e+02 | 1.645000e+02 | 1.687500e+02 | 1.707500e+02 | 1.707500e+02 | 1.707500e+02 | 1.707500e+02 | 1.762500e+02 | 1.762500e+02 | 1.770000e+02 | 1.770000e+02 | 1.770000e+02 | 1.770000e+02 | 1.777500e+02 | 1.830000e+02 | 1.830000e+02 | 1.852500e+02 | 1.852500e+02 | 1.860000e+02 | 1.860000e+02 | 1.860000e+02 | 1.860000e+02 | 1.860000e+02 | 1.860000e+02 | 1.860000e+02 | 1.932500e+02 | 1.940000e+02 | 1.940000e+02 | 2.017500e+02 | 2.030000e+02 | 2.037500e+02 | 2.077500e+02 | 2.137500e+02 | 2.215000e+02 | 2.280000e+02 | 2.280000e+02 | 2.280000e+02 | 2.280000e+02 | 2.280000e+02 | 2.285000e+02 | 2.380000e+02 | 2.520000e+02 | 2.527500e+02 | 2.572500e+02 | 2.572500e+02 | 2.572500e+02 | 2.605000e+02 | 2.662500e+02 | 2.702500e+02 | 2.725000e+02 | 2.820000e+02 | 2.947500e+02 | 2.947500e+02 | 3.025000e+02 | 3.025000e+02 | 3.040000e+02 | 3.040000e+02 | 3.040000e+02 | 3.040000e+02 | 3.040000e+02 | 3.307500e+02 | 3.307500e+02 | 3.312500e+02 | 3.312500e+02 | 3.315000e+02 | 3.315000e+02 | 3.317500e+02 | 3.317500e+02 | 3.317500e+02 | 3.345000e+02 | 3.345000e+02 | 3.345000e+02 | 3.352500e+02 | 3.352500e+02 | 3.400000e+02 | 3.400000e+02 | 3.550000e+02 | 3.550000e+02 | 3.550000e+02 | 3.550000e+02 | 3.555000e+02 | 3.577500e+02 | 3.580000e+02 | 3.587500e+02 | 3.595000e+02 | 3.605000e+02 | 3.605000e+02 | 3.605000e+02 | 3.617500e+02 | 3.647500e+02 | 3.665000e+02 | 3.665000e+02 | 3.670000e+02 | 3.730000e+02 | 3.730000e+02 | 3.737500e+02 | 3.825000e+02 | 3.945000e+02 | 3.985000e+02 | 4.077500e+02 | 4.085000e+02 | 4.085000e+02 | 4.085000e+02 | 4.085000e+02 | 4.102500e+02 | 4.117500e+02 | 4.147500e+02 | 4.155000e+02 | 4.155000e+02 | 4.155000e+02 | 4.155000e+02 | 4.207500e+02 | 4.207500e+02 | 4.312500e+02 | 4.327500e+02 | 4.332500e+02 | 4.332500e+02 | 4.335000e+02 | 4.352500e+02 | 4.417500e+02 | 4.425000e+02 | 4.417500e+02 | 4.450000e+02 | 4.495000e+02 | 4.530000e+02 | 4.592500e+02 | 4.607500e+02 | 4.610000e+02 | 4.610000e+02 | 4.737500e+02 | 4.740000e+02 | 4.747500e+02 | 4.777500e+02 | 4.787500e+02 | 4.890000e+02 | 4.897500e+02 | 4.907500e+02 | 4.955000e+02 | 4.955000e+02 | 4.960000e+02 | 4.960000e+02 | 4.960000e+02 | 4.967500e+02 | 5.062500e+02 | 5.062500e+02 | 5.080000e+02 | 5.225000e+02 | 5.225000e+02 | 5.405000e+02 | 5.405000e+02 | 5.450000e+02 | 5.457500e+02 | 5.457500e+02 | 5.465000e+02 | 5.487500e+02 | 5.480000e+02 | 5.487500e+02 | 5.487500e+02 | 5.487500e+02 | 5.500000e+02 | 5.515000e+02 | 5.535000e+02 | 5.540000e+02 | 5.550000e+02 | 5.555000e+02 | 5.567500e+02 | 5.570000e+02 | 5.580000e+02 | 5.600000e+02 | 5.602500e+02 | 5.615000e+02 | 5.617500e+02 | 5.620000e+02 | 5.660000e+02 | 5.705000e+02 | 5.705000e+02 | 5.742500e+02 | 5.720000e+02 | 5.750000e+02 | 5.765000e+02 | 5.807500e+02 | 5.822500e+02 | 5.850000e+02 | 5.900000e+02 | 5.915000e+02 | 5.922500e+02 | 5.995000e+02 | 6.027500e+02 | 6.085000e+02 | 6.085000e+02 | 6.200000e+02 | 6.215000e+02 | 6.322500e+02 | 6.572500e+02 | 6.910000e+02 | 7.055000e+02 | 7.057500e+02 | 7.057500e+02 | 7.057500e+02 | 7.057500e+02 | 7.555000e+02 | 8.027500e+02 | 8.035000e+02 | 8.035000e+02 | 8.035000e+02 | 8.035000e+02 | 8.120000e+02 | 8.120000e+02 | 8.167500e+02 | 8.167500e+02 | 8.470000e+02 | 8.492500e+02 | 8.500000e+02 | 8.635000e+02 | 8.635000e+02 | 8.635000e+02 | 8.637500e+02 | 8.682500e+02 | 8.690000e+02 | 8.690000e+02 | 8.700000e+02 | 8.715000e+02 | 8.715000e+02 | 8.715000e+02 | 8.865000e+02 | 8.865000e+02 | 8.895000e+02 | 8.935000e+02 | 8.937500e+02 | 8.937500e+02 | 9.017500e+02 | 9.017500e+02 | 9.020000e+02 | 9.020000e+02 | 9.022500e+02 | 9.025000e+02 | 9.030000e+02 | 9.030000e+02 | 9.032500e+02 | 9.085000e+02 | 9.225000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.295000e+02 | 9.297500e+02 | 9.297500e+02 | 9.342500e+02 | 9.485000e+02 | 9.590000e+02 | 9.605000e+02 | 9.657500e+02 | 9.707500e+02 | 9.795000e+02 | 9.795000e+02 | 9.830000e+02 | 9.830000e+02 | 9.837500e+02 | 9.875000e+02 | 9.915000e+02 | 9.915000e+02 | 9.970000e+02 | 1.001500e+03 | 1.002250e+03 | 1.003000e+03 | 1.003000e+03 | 1.003750e+03 | 1.005250e+03 | 1.007500e+03 | 1.008250e+03 | 1.009750e+03 | 1.020500e+03 | 1.021250e+03 | 1.022500e+03 | 1.029500e+03 | 1.038250e+03 | 1.042000e+03 | 1.044500e+03 | 1.046000e+03 | 1.046750e+03 | 1.048250e+03 | 1.048250e+03 | 1.048500e+03 | 1.051500e+03 | 1.053000e+03 | 1.053750e+03 | 1.054250e+03 | 1.060750e+03 | 1.064500e+03 | 1.065250e+03 | 1.066750e+03 | 1.068250e+03 | 1.069000e+03 | 1.071250e+03 | 1.072750e+03 | 1.075750e+03 | 1.078750e+03 | 1.081750e+03 | 1.087000e+03 | 1.089500e+03 | 1.093500e+03 | 1.098000e+03 | 1.103500e+03 | 1.186250e+03 | 1.188750e+03 | 1.191250e+03 | 1.192500e+03 | 1.194500e+03 | 1.198750e+03 | 1.202000e+03 | 1.206250e+03 | 1.220750e+03 | 1.232750e+03 | 1.242250e+03 | 1.252250e+03 | 1.254500e+03 | 1.263750e+03 | 1.271250e+03 | 1.286750e+03 | 1.304750e+03 | 1.306250e+03 | 1.308750e+03 | 1.312000e+03 | 1.316000e+03 | 1.316250e+03 | 1.316250e+03 | 1.316250e+03 | 1.316250e+03 | 1.316250e+03 | 1.316250e+03 | 1.328250e+03 | 1.335000e+03 | 1.346250e+03 | 1.351500e+03 |
| 50% | 21.607878 | 20.921188 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 4.000000 | 5.000000 | 7.000000 | 10.000000 | 13.500000 | 18.500000 | 21.500000 | 28.500000 | 40.000000 | 47.000000 | 54.500000 | 61.500000 | 74.000000 | 79.500000 | 91.000000 | 98.500000 | 109.500000 | 127.500000 | 135.500000 | 139.000000 | 146.000000 | 157.000000 | 164.000000 | 175.000000 | 183.000000 | 184.500000 | 199.500000 | 219.500000 | 228.500000 | 242.500000 | 246.000000 | 255.000000 | 255.000000 | 256.000000 | 267.500000 | 277.500000 | 282.500000 | 284.500000 | 302.000000 | 310.500000 | 321.000000 | 328.000000 | 333.500000 | 346.000000 | 354.000000 | 3.635000e+02 | 3.650000e+02 | 3.900000e+02 | 4.210000e+02 | 4.305000e+02 | 4.420000e+02 | 4.630000e+02 | 4.690000e+02 | 4.765000e+02 | 4.790000e+02 | 4.990000e+02 | 5.445000e+02 | 5.525000e+02 | 5.565000e+02 | 5.615000e+02 | 5.670000e+02 | 5.690000e+02 | 5.825000e+02 | 5.915000e+02 | 5.920000e+02 | 5.925000e+02 | 6.055000e+02 | 6.240000e+02 | 6.365000e+02 | 6.375000e+02 | 6.570000e+02 | 6.600000e+02 | 6.715000e+02 | 6.990000e+02 | 7.250000e+02 | 7.330000e+02 | 7.570000e+02 | 7.615000e+02 | 7.615000e+02 | 7.805000e+02 | 7.930000e+02 | 8.350000e+02 | 8.395000e+02 | 8.420000e+02 | 8.675000e+02 | 8.700000e+02 | 8.705000e+02 | 8.710000e+02 | 8.715000e+02 | 8.780000e+02 | 9.045000e+02 | 9.235000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.395000e+02 | 9.440000e+02 | 9.895000e+02 | 9.915000e+02 | 1.007000e+03 | 1.018000e+03 | 1.040000e+03 | 1.061500e+03 | 1.070500e+03 | 1.071000e+03 | 1.071000e+03 | 1.071000e+03 | 1.074000e+03 | 1.081500e+03 | 1.087000e+03 | 1.096500e+03 | 1.115000e+03 | 1.151500e+03 | 1.155000e+03 | 1.189500e+03 | 1.206500e+03 | 1.210500e+03 | 1.242000e+03 | 1.273000e+03 | 1.273000e+03 | 1.281500e+03 | 1.307000e+03 | 1.369000e+03 | 1.391500e+03 | 1.432500e+03 | 1.444000e+03 | 1.462500e+03 | 1.478500e+03 | 1.539000e+03 | 1.573000e+03 | 1.586000e+03 | 1.612500e+03 | 1.614500e+03 | 1.616500e+03 | 1.641000e+03 | 1.642500e+03 | 1.665000e+03 | 1.706000e+03 | 1.708500e+03 | 1.710500e+03 | 1.723500e+03 | 1.725000e+03 | 1.727500e+03 | 1.742000e+03 | 1.745000e+03 | 1.750500e+03 | 1.774500e+03 | 1.784500e+03 | 1.810500e+03 | 1.847000e+03 | 1.880500e+03 | 1.916500e+03 | 1.931000e+03 | 1.945500e+03 | 1.996500e+03 | 2.025000e+03 | 2.042000e+03 | 2.070500e+03 | 2.138500e+03 | 2.160000e+03 | 2.185500e+03 | 2.216000e+03 | 2.258000e+03 | 2.265000e+03 | 2.274000e+03 | 2.284000e+03 | 2.365000e+03 | 2.417000e+03 | 2.427000e+03 | 2.473500e+03 | 2.487000e+03 | 2.496000e+03 | 2.517500e+03 | 2.530000e+03 | 2.545500e+03 | 2.587000e+03 | 2.725500e+03 | 2.779000e+03 | 2.905000e+03 | 3.028000e+03 | 3.063000e+03 | 3.168000e+03 | 3.247000e+03 | 3.351000e+03 | 3.408000e+03 | 3.443000e+03 | 3.443000e+03 | 3.504000e+03 | 3.518500e+03 | 3.527000e+03 | 3.580000e+03 | 3.625500e+03 | 3.737500e+03 | 3.737500e+03 | 3.761500e+03 | 3.855000e+03 | 3.892500e+03 | 3.978000e+03 | 3.995500e+03 | 4.108000e+03 | 4.126000e+03 | 4.128500e+03 | 4.170000e+03 | 4.250500e+03 | 4.323000e+03 | 4.328000e+03 | 4.382000e+03 | 4.384500e+03 | 4.387500e+03 | 4.459500e+03 | 4.467000e+03 | 4.499500e+03 | 4.502000e+03 | 4.815500e+03 | 4.815500e+03 | 4.815500e+03 | 4.888500e+03 | 4.896000e+03 | 5.020500e+03 | 5.045500e+03 | 5.111500e+03 | 5.114000e+03 | 5.114000e+03 | 5.129500e+03 | 5.135500e+03 | 5.168000e+03 | 5.243500e+03 | 5.305000e+03 | 5.306500e+03 | 5.310000e+03 | 5.313000e+03 | 5.346500e+03 | 5.348000e+03 | 5.409500e+03 | 5.574000e+03 | 5.598000e+03 | 5.599500e+03 | 5.609000e+03 | 5.647000e+03 | 5.648000e+03 | 5.651000e+03 | 5.693000e+03 | 5.693000e+03 | 5.766500e+03 | 5.787500e+03 | 5.838000e+03 | 5.862000e+03 | 5.863500e+03 | 5.903500e+03 | 6.104000e+03 | 6.111000e+03 | 6.121000e+03 | 6.132000e+03 | 6.145000e+03 | 6.174500e+03 | 6.200500e+03 | 6.264500e+03 | 6.324000e+03 | 6.366500e+03 | 6.419000e+03 | 6.477000e+03 | 6.657000e+03 | 6.733000e+03 | 6.883500e+03 | 6.973500e+03 | 7.029500e+03 | 7.101000e+03 | 7.170500e+03 | 7.240500e+03 | 7.306500e+03 | 7.367000e+03 | 7.439500e+03 | 7.502500e+03 | 7.540000e+03 | 7.580000e+03 | 7.595500e+03 | 7.626000e+03 | 7.646500e+03 | 7.661500e+03 | 7.683500e+03 | 7.743500e+03 | 7.828000e+03 | 7.910000e+03 | 7.979000e+03 | 8.092000e+03 | 8.227000e+03 | 8.320500e+03 | 8.374000e+03 | 8.435000e+03 | 8.501500e+03 | 8.540000e+03 | 8.593500e+03 | 8.648000e+03 | 8.907000e+03 | 9.002000e+03 | 9.066000e+03 | 9.140000e+03 | 9.162500e+03 | 9.229000e+03 | 9.244500e+03 | 9.321000e+03 | 9.351000e+03 | 9.392500e+03 | 9.616500e+03 | 9.675500e+03 | 9.700500e+03 | 9.728000e+03 | 9.841500e+03 | 9.869500e+03 | 9.958500e+03 | 1.002400e+04 | 1.010300e+04 | 1.014350e+04 | 1.018150e+04 | 1.028450e+04 | 1.031650e+04 | 1.040900e+04 | 1.045650e+04 | 1.058550e+04 | 1.066400e+04 | 1.071500e+04 | 1.086000e+04 | 1.092800e+04 | 1.104350e+04 | 1.109100e+04 | 1.130000e+04 | 1.134200e+04 | 1.137100e+04 | 1.155750e+04 | 1.160150e+04 | 1.170800e+04 | 1.188700e+04 | 1.207500e+04 | 1.208050e+04 | 1.214400e+04 | 1.215900e+04 | 1.219000e+04 | 1.220050e+04 | 1.220650e+04 | 1.221650e+04 | 1.224900e+04 | 1.225900e+04 | 1.228200e+04 | 1.228650e+04 | 1.231150e+04 | 1.238400e+04 | 1.238400e+04 | 1.242300e+04 | 1.242300e+04 | 1.243200e+04 | 1.245450e+04 | 1.282800e+04 | 1.283050e+04 | 1.283050e+04 | 1.283050e+04 | 1.283050e+04 | 1.283050e+04 | 1.283050e+04 | 1.285950e+04 | 1.295100e+04 | 1.297000e+04 | 1.297000e+04 | 1.297000e+04 | 1.297000e+04 | 1.298600e+04 | 1.298850e+04 | 1.299700e+04 | 1.299800e+04 | 1.300400e+04 | 1.301100e+04 | 1.301100e+04 | 1.301500e+04 | 1.302000e+04 | 1.302000e+04 | 1.302200e+04 | 1.302200e+04 | 1.302200e+04 | 1.302200e+04 | 1.303300e+04 | 1.303300e+04 | 1.304100e+04 | 1.304950e+04 | 1.305550e+04 | 1.306250e+04 | 1.306650e+04 | 1.307650e+04 | 1.309050e+04 | 1.340100e+04 | 1.374550e+04 | 1.374550e+04 | 1.416100e+04 | 1.495550e+04 | 1.542850e+04 | 1.550700e+04 | 1.555700e+04 | 1.559550e+04 | 1.563650e+04 | 1.569450e+04 | 1.570250e+04 | 1.570250e+04 | 1.581700e+04 | 1.582550e+04 | 1.585550e+04 | 1.590000e+04 | 1.596350e+04 | 1.604700e+04 | 1.611450e+04 | 1.613050e+04 | 1.614600e+04 | 1.615850e+04 | 1.617200e+04 | 1.654100e+04 | 1.691050e+04 | 1.738000e+04 | 1.771900e+04 | 1.804700e+04 | 1.832650e+04 | 1.855650e+04 | 1.857350e+04 | 1.858650e+04 | 1.858900e+04 | 1.861850e+04 | 1.863200e+04 | 1.864600e+04 | 1.866250e+04 | 1.866750e+04 | 1.866950e+04 | 1.867650e+04 | 1.868250e+04 | 1.869000e+04 | 1.888950e+04 | 1.897900e+04 | 1.899650e+04 | 1.901950e+04 | 1.904750e+04 | 1.907100e+04 | 1.909950e+04 | 1.913300e+04 | 1.916150e+04 |
| 75% | 40.950592 | 83.380449 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.250000 | 2.000000 | 2.000000 | 3.000000 | 5.250000 | 6.000000 | 7.500000 | 10.000000 | 12.250000 | 16.000000 | 17.250000 | 20.750000 | 26.250000 | 32.250000 | 45.750000 | 49.750000 | 75.250000 | 89.250000 | 105.500000 | 120.000000 | 133.000000 | 145.250000 | 180.000000 | 204.250000 | 242.000000 | 283.750000 | 325.500000 | 383.000000 | 422.500000 | 488.750000 | 539.750000 | 573.750000 | 612.000000 | 647.750000 | 698.000000 | 767.250000 | 810.250000 | 869.250000 | 923.500000 | 968.250000 | 997.250000 | 1028.250000 | 1072.250000 | 1144.750000 | 1202.750000 | 1265.500000 | 1267.250000 | 1285.750000 | 1307.000000 | 1323.000000 | 1410.000000 | 1464.750000 | 1512.250000 | 1545.500000 | 1575.750000 | 1587.000000 | 1618.250000 | 1686.750000 | 1728.250000 | 1769.750000 | 1799.000000 | 1.817750e+03 | 1.882250e+03 | 1.955500e+03 | 2.074500e+03 | 2.145750e+03 | 2.183000e+03 | 2.202250e+03 | 2.386250e+03 | 2.478000e+03 | 2.525750e+03 | 2.576750e+03 | 2.625000e+03 | 2.686750e+03 | 2.728750e+03 | 2.821250e+03 | 2.925750e+03 | 3.020750e+03 | 3.031250e+03 | 3.081750e+03 | 3.113250e+03 | 3.147250e+03 | 3.422500e+03 | 3.520750e+03 | 3.606250e+03 | 3.668000e+03 | 3.722250e+03 | 3.780250e+03 | 3.960500e+03 | 4.042000e+03 | 4.096500e+03 | 4.251500e+03 | 4.449000e+03 | 4.643250e+03 | 4.827750e+03 | 5.115750e+03 | 5.342500e+03 | 5.541750e+03 | 5.707500e+03 | 5.948500e+03 | 6.099500e+03 | 6.314250e+03 | 6.490500e+03 | 6.587750e+03 | 6.957500e+03 | 7.099000e+03 | 7.127000e+03 | 7.141250e+03 | 7.161000e+03 | 7.186250e+03 | 7.278250e+03 | 7.446750e+03 | 7.582500e+03 | 7.891000e+03 | 8.083500e+03 | 8.451000e+03 | 8.574000e+03 | 8.614750e+03 | 8.635500e+03 | 8.644000e+03 | 8.647000e+03 | 8.762250e+03 | 8.870500e+03 | 8.916500e+03 | 8.950000e+03 | 9.002250e+03 | 9.047000e+03 | 9.201750e+03 | 9.469250e+03 | 9.693750e+03 | 9.830250e+03 | 9.915750e+03 | 1.013000e+04 | 1.027075e+04 | 1.045400e+04 | 1.065975e+04 | 1.091825e+04 | 1.152925e+04 | 1.180100e+04 | 1.184225e+04 | 1.248450e+04 | 1.265450e+04 | 1.311900e+04 | 1.331600e+04 | 1.345750e+04 | 1.378225e+04 | 1.395125e+04 | 1.409600e+04 | 1.418425e+04 | 1.446750e+04 | 1.499975e+04 | 1.535875e+04 | 1.570150e+04 | 1.593525e+04 | 1.626975e+04 | 1.677500e+04 | 1.732375e+04 | 1.744100e+04 | 1.776250e+04 | 1.820450e+04 | 1.865575e+04 | 1.910975e+04 | 1.956950e+04 | 2.002100e+04 | 2.046325e+04 | 2.082550e+04 | 2.118050e+04 | 2.151300e+04 | 2.184275e+04 | 2.214325e+04 | 2.245450e+04 | 2.275900e+04 | 2.302650e+04 | 2.327825e+04 | 2.355375e+04 | 2.380875e+04 | 2.408075e+04 | 2.434050e+04 | 2.458050e+04 | 2.477975e+04 | 2.498200e+04 | 2.516600e+04 | 2.536325e+04 | 2.555325e+04 | 2.570875e+04 | 2.589900e+04 | 2.601775e+04 | 2.615625e+04 | 2.627550e+04 | 2.642025e+04 | 2.693200e+04 | 2.761900e+04 | 2.808575e+04 | 2.843475e+04 | 2.892725e+04 | 2.992775e+04 | 3.026875e+04 | 3.058175e+04 | 3.088500e+04 | 3.112250e+04 | 3.134075e+04 | 3.163075e+04 | 3.231525e+04 | 3.271125e+04 | 3.298200e+04 | 3.316800e+04 | 3.351200e+04 | 3.406150e+04 | 3.473275e+04 | 3.526600e+04 | 3.594900e+04 | 3.667525e+04 | 3.734625e+04 | 3.788375e+04 | 3.825725e+04 | 3.840625e+04 | 3.859700e+04 | 3.935950e+04 | 3.954100e+04 | 3.958075e+04 | 3.961300e+04 | 3.974300e+04 | 3.978375e+04 | 3.992225e+04 | 4.020850e+04 | 4.054500e+04 | 4.140575e+04 | 4.172350e+04 | 4.257400e+04 | 4.346850e+04 | 4.436550e+04 | 4.534675e+04 | 4.618225e+04 | 4.661875e+04 | 4.686750e+04 | 4.717875e+04 | 4.765400e+04 | 4.828600e+04 | 4.901600e+04 | 4.990350e+04 | 5.053025e+04 | 5.165600e+04 | 5.375275e+04 | 5.533375e+04 | 5.671400e+04 | 5.708850e+04 | 5.744850e+04 | 5.800950e+04 | 5.823075e+04 | 5.914500e+04 | 6.036650e+04 | 6.134250e+04 | 6.336675e+04 | 6.364250e+04 | 6.393275e+04 | 6.474175e+04 | 6.587000e+04 | 6.652150e+04 | 6.758075e+04 | 6.872200e+04 | 6.966000e+04 | 7.068900e+04 | 7.253950e+04 | 7.375275e+04 | 7.484375e+04 | 7.590825e+04 | 7.703200e+04 | 7.851075e+04 | 7.986225e+04 | 8.087950e+04 | 8.143075e+04 | 8.188625e+04 | 8.318100e+04 | 8.422075e+04 | 8.549050e+04 | 8.654450e+04 | 8.702675e+04 | 8.790200e+04 | 8.860400e+04 | 8.986100e+04 | 9.118350e+04 | 9.328550e+04 | 9.513700e+04 | 9.658525e+04 | 9.831725e+04 | 9.992625e+04 | 1.012665e+05 | 1.030355e+05 | 1.045850e+05 | 1.060528e+05 | 1.071865e+05 | 1.086910e+05 | 1.099095e+05 | 1.113105e+05 | 1.125618e+05 | 1.136800e+05 | 1.147015e+05 | 1.154432e+05 | 1.163560e+05 | 1.170340e+05 | 1.176775e+05 | 1.193742e+05 | 1.201908e+05 | 1.208795e+05 | 1.215632e+05 | 1.221305e+05 | 1.226868e+05 | 1.232118e+05 | 1.237690e+05 | 1.243555e+05 | 1.247495e+05 | 1.251908e+05 | 1.258028e+05 | 1.261730e+05 | 1.277805e+05 | 1.286055e+05 | 1.290965e+05 | 1.323020e+05 | 1.347988e+05 | 1.368142e+05 | 1.385962e+05 | 1.415465e+05 | 1.438888e+05 | 1.457762e+05 | 1.469178e+05 | 1.474092e+05 | 1.476095e+05 | 1.478592e+05 | 1.481622e+05 | 1.484932e+05 | 1.488132e+05 | 1.491510e+05 | 1.494905e+05 | 1.497592e+05 | 1.500750e+05 | 1.505028e+05 | 1.509705e+05 | 1.514090e+05 | 1.518852e+05 | 1.523562e+05 | 1.527405e+05 | 1.531638e+05 | 1.537502e+05 | 1.543348e+05 | 1.549072e+05 | 1.553618e+05 | 1.559360e+05 | 1.565762e+05 | 1.576045e+05 | 1.584482e+05 | 1.592850e+05 | 1.601492e+05 | 1.608180e+05 | 1.615410e+05 | 1.619480e+05 | 1.625830e+05 | 1.633210e+05 | 1.641580e+05 | 1.648812e+05 | 1.655605e+05 | 1.659298e+05 | 1.664218e+05 | 1.669292e+05 | 1.675782e+05 | 1.683568e+05 | 1.689890e+05 | 1.695608e+05 | 1.702638e+05 | 1.707425e+05 | 1.712928e+05 | 1.719325e+05 | 1.722790e+05 | 1.727595e+05 | 1.728700e+05 | 1.733292e+05 | 1.739665e+05 | 1.744362e+05 | 1.746805e+05 | 1.754660e+05 | 1.763212e+05 | 1.772742e+05 | 1.781922e+05 | 1.787922e+05 | 1.795472e+05 | 1.804732e+05 | 1.814368e+05 | 1.828598e+05 | 1.845822e+05 | 1.861455e+05 | 1.875015e+05 | 1.883148e+05 | 1.894488e+05 | 1.907998e+05 | 1.920362e+05 | 1.932550e+05 | 1.944860e+05 | 1.950452e+05 | 1.953518e+05 | 1.957335e+05 | 1.959082e+05 | 1.967778e+05 | 1.971635e+05 | 1.980970e+05 | 1.987168e+05 | 1.992678e+05 | 2.000322e+05 | 2.009968e+05 | 2.021172e+05 | 2.031902e+05 | 2.043992e+05 | 2.049255e+05 | 2.053220e+05 | 2.070240e+05 | 2.085855e+05 | 2.099700e+05 | 2.116600e+05 | 2.131012e+05 | 2.136340e+05 | 2.140832e+05 | 2.155352e+05 | 2.169722e+05 | 2.185522e+05 | 2.200062e+05 | 2.213728e+05 | 2.219042e+05 | 2.223328e+05 | 2.233980e+05 | 2.248478e+05 | 2.263180e+05 | 2.275818e+05 | 2.285038e+05 | 2.289218e+05 | 2.293315e+05 | 2.305220e+05 | 2.316930e+05 | 2.328330e+05 | 2.340570e+05 | 2.352302e+05 | 2.356760e+05 | 2.361180e+05 | 2.371345e+05 | 2.384932e+05 | 2.396202e+05 | 2.409568e+05 | 2.419678e+05 | 2.424570e+05 | 2.439068e+05 | 2.465390e+05 | 2.492698e+05 | 2.504960e+05 | 2.512458e+05 | 2.518208e+05 | 2.522262e+05 | 2.525150e+05 | 2.527770e+05 | 2.530820e+05 | 2.533535e+05 | 2.535725e+05 | 2.540952e+05 |
| max | 71.706900 | 178.065000 | 444.000000 | 444.000000 | 549.000000 | 761.000000 | 1058.000000 | 1423.000000 | 3554.000000 | 3554.000000 | 4903.000000 | 5806.000000 | 7153.000000 | 11177.000000 | 13522.000000 | 16678.000000 | 19665.000000 | 22112.000000 | 24953.000000 | 27100.000000 | 29631.000000 | 31728.000000 | 33366.000000 | 33366.000000 | 48206.000000 | 54406.000000 | 56249.000000 | 58182.000000 | 59989.000000 | 61682.000000 | 62031.000000 | 62442.000000 | 62662.000000 | 64084.000000 | 64084.000000 | 64287.000000 | 64786.000000 | 65187.000000 | 65596.000000 | 65914.000000 | 66337.000000 | 66907.000000 | 67103.000000 | 67217.000000 | 67332.000000 | 67466.000000 | 67592.000000 | 67666.000000 | 67707.000000 | 67743.000000 | 67760.000000 | 67773.000000 | 67781.000000 | 67786.000000 | 67790.000000 | 67794.000000 | 67798.000000 | 67799.000000 | 67800.000000 | 67800.000000 | 67800.000000 | 67800.000000 | 67800.000000 | 67800.000000 | 69176.000000 | 74386.000000 | 86693.000000 | 105383.000000 | 125013.000000 | 143912.000000 | 165987.000000 | 192301.000000 | 224560.000000 | 256792.000000 | 289087.000000 | 321477.000000 | 351354.000000 | 382747.000000 | 413516.000000 | 444731.000000 | 480667.000000 | 515081.000000 | 544183.000000 | 571440.000000 | 598380.000000 | 627205.000000 | 652611.000000 | 682626.000000 | 715656.000000 | 743588.000000 | 769684.000000 | 799512.000000 | 825429.000000 | 854288.000000 | 887858.000000 | 920185.000000 | 950581.000000 | 977082.000000 | 1.000785e+06 | 1.025362e+06 | 1.051800e+06 | 1.081020e+06 | 1.115946e+06 | 1.143296e+06 | 1.167593e+06 | 1.191678e+06 | 1.216209e+06 | 1.240769e+06 | 1.268180e+06 | 1.295019e+06 | 1.320155e+06 | 1.339022e+06 | 1.358293e+06 | 1.381241e+06 | 1.401649e+06 | 1.428467e+06 | 1.453214e+06 | 1.477373e+06 | 1.495736e+06 | 1.518126e+06 | 1.539133e+06 | 1.561830e+06 | 1.587596e+06 | 1.611253e+06 | 1.632364e+06 | 1.652431e+06 | 1.671104e+06 | 1.690754e+06 | 1.709303e+06 | 1.731625e+06 | 1.756098e+06 | 1.779731e+06 | 1.798718e+06 | 1.816154e+06 | 1.837656e+06 | 1.857511e+06 | 1.879150e+06 | 1.904550e+06 | 1.925710e+06 | 1.943626e+06 | 1.961263e+06 | 1.979647e+06 | 2.000757e+06 | 2.023890e+06 | 2.048756e+06 | 2.073964e+06 | 2.092912e+06 | 2.112731e+06 | 2.136401e+06 | 2.163465e+06 | 2.191991e+06 | 2.223553e+06 | 2.255823e+06 | 2.280971e+06 | 2.313123e+06 | 2.350198e+06 | 2.386074e+06 | 2.426391e+06 | 2.472385e+06 | 2.513731e+06 | 2.554461e+06 | 2.595744e+06 | 2.642174e+06 | 2.693993e+06 | 2.750622e+06 | 2.801983e+06 | 2.847664e+06 | 2.898432e+06 | 2.941517e+06 | 3.002171e+06 | 3.062290e+06 | 3.124786e+06 | 3.192841e+06 | 3.252874e+06 | 3.311312e+06 | 3.370208e+06 | 3.438244e+06 | 3.506364e+06 | 3.582184e+06 | 3.654445e+06 | 3.716980e+06 | 3.777456e+06 | 3.839546e+06 | 3.904066e+06 | 3.974630e+06 | 4.043070e+06 | 4.116393e+06 | 4.181308e+06 | 4.236083e+06 | 4.292934e+06 | 4.359391e+06 | 4.431244e+06 | 4.498701e+06 | 4.567420e+06 | 4.623604e+06 | 4.669149e+06 | 4.714678e+06 | 4.773479e+06 | 4.827936e+06 | 4.887293e+06 | 4.946590e+06 | 5.000709e+06 | 5.046463e+06 | 5.094087e+06 | 5.142088e+06 | 5.198137e+06 | 5.249451e+06 | 5.314791e+06 | 5.361712e+06 | 5.400904e+06 | 5.437580e+06 | 5.482614e+06 | 5.529973e+06 | 5.574013e+06 | 5.622842e+06 | 5.665887e+06 | 5.700119e+06 | 5.736641e+06 | 5.777001e+06 | 5.822167e+06 | 5.867547e+06 | 5.914395e+06 | 5.957126e+06 | 5.991507e+06 | 6.026895e+06 | 6.068759e+06 | 6.109773e+06 | 6.153983e+06 | 6.204376e+06 | 6.247464e+06 | 6.278633e+06 | 6.302200e+06 | 6.329593e+06 | 6.363650e+06 | 6.399723e+06 | 6.447501e+06 | 6.488563e+06 | 6.522914e+06 | 6.557342e+06 | 6.596849e+06 | 6.635867e+06 | 6.681004e+06 | 6.730288e+06 | 6.772447e+06 | 6.810862e+06 | 6.862834e+06 | 6.902696e+06 | 6.941758e+06 | 6.988869e+06 | 7.037151e+06 | 7.081803e+06 | 7.119311e+06 | 7.152546e+06 | 7.195994e+06 | 7.235428e+06 | 7.281081e+06 | 7.336043e+06 | 7.384578e+06 | 7.420293e+06 | 7.459742e+06 | 7.504998e+06 | 7.556060e+06 | 7.614653e+06 | 7.671034e+06 | 7.725952e+06 | 7.771893e+06 | 7.813735e+06 | 7.865983e+06 | 7.925748e+06 | 7.990636e+06 | 8.059782e+06 | 8.116518e+06 | 8.165858e+06 | 8.233610e+06 | 8.295581e+06 | 8.358864e+06 | 8.435164e+06 | 8.517113e+06 | 8.599842e+06 | 8.661982e+06 | 8.729385e+06 | 8.806228e+06 | 8.885632e+06 | 8.976684e+06 | 9.075924e+06 | 9.165619e+06 | 9.270467e+06 | 9.355775e+06 | 9.482891e+06 | 9.587499e+06 | 9.716853e+06 | 9.844858e+06 | 9.972308e+06 | 1.008738e+07 | 1.020795e+07 | 1.034845e+07 | 1.049508e+07 | 1.065991e+07 | 1.084030e+07 | 1.100806e+07 | 1.114429e+07 | 1.130723e+07 | 1.147116e+07 | 1.164433e+07 | 1.183588e+07 | 1.203418e+07 | 1.221345e+07 | 1.236024e+07 | 1.253468e+07 | 1.271020e+07 | 1.289348e+07 | 1.300581e+07 | 1.321400e+07 | 1.336953e+07 | 1.350976e+07 | 1.367033e+07 | 1.385855e+07 | 1.406111e+07 | 1.428472e+07 | 1.451751e+07 | 1.473305e+07 | 1.491406e+07 | 1.510892e+07 | 1.533341e+07 | 1.555595e+07 | 1.578746e+07 | 1.602744e+07 | 1.624503e+07 | 1.643273e+07 | 1.662755e+07 | 1.683656e+07 | 1.708326e+07 | 1.732298e+07 | 1.757495e+07 | 1.776686e+07 | 1.795468e+07 | 1.815372e+07 | 1.835174e+07 | 1.858135e+07 | 1.877556e+07 | 1.887320e+07 | 1.909949e+07 | 1.925513e+07 | 1.942976e+07 | 1.963001e+07 | 1.986370e+07 | 2.009936e+07 | 2.025299e+07 | 2.055330e+07 | 2.076205e+07 | 2.094633e+07 | 2.118144e+07 | 2.143688e+07 | 2.171517e+07 | 2.201039e+07 | 2.227108e+07 | 2.248433e+07 | 2.269933e+07 | 2.292625e+07 | 2.315661e+07 | 2.339232e+07 | 2.363505e+07 | 2.383673e+07 | 2.401451e+07 | 2.415792e+07 | 2.433463e+07 | 2.451787e+07 | 2.471168e+07 | 2.490244e+07 | 2.507305e+07 | 2.520411e+07 | 2.535608e+07 | 2.550362e+07 | 2.565757e+07 | 2.582618e+07 | 2.599274e+07 | 2.613506e+07 | 2.624705e+07 | 2.638226e+07 | 2.649759e+07 | 2.661923e+07 | 2.674320e+07 | 2.687760e+07 | 2.698159e+07 | 2.707124e+07 | 2.716155e+07 | 2.725718e+07 | 2.735236e+07 | 2.745812e+07 | 2.755776e+07 | 2.764488e+07 | 2.770990e+07 | 2.776409e+07 | 2.782681e+07 | 2.789692e+07 | 2.796685e+07 | 2.804614e+07 | 2.811767e+07 | 2.817475e+07 | 2.823097e+07 | 2.830323e+07 | 2.837796e+07 | 2.845547e+07 | 2.853281e+07 | 2.859739e+07 | 2.864874e+07 | 2.870697e+07 | 2.876403e+07 | 2.883123e+07 | 2.889928e+07 | 2.896573e+07 | 2.902393e+07 | 2.906494e+07 | 2.910997e+07 | 2.916762e+07 | 2.922554e+07 | 2.928801e+07 | 2.934953e+07 | 2.940246e+07 | 2.944069e+07 | 2.949735e+07 | 2.955131e+07 | 2.961044e+07 | 2.967098e+07 | 2.973261e+07 | 2.978799e+07 | 2.982175e+07 | 2.987335e+07 | 2.992695e+07 | 3.001391e+07 | 3.008138e+07 | 3.015870e+07 | 3.022140e+07 | 3.026449e+07 | 3.033392e+07 | 3.039517e+07 | 3.046221e+07 | 3.054126e+07 | 3.061109e+07 | 3.067415e+07 | 3.070912e+07 | 3.078680e+07 | 3.084735e+07 | 3.092239e+07 | 3.100226e+07 | 3.108496e+07 | 3.115150e+07 | 3.119788e+07 | 3.126811e+07 | 3.134598e+07 | 3.142136e+07 | 3.149565e+07 | 3.157564e+07 | 3.162801e+07 | 3.167003e+07 | 3.173796e+07 | 3.179924e+07 | 3.186209e+07 | 3.192935e+07 | 3.199175e+07 | 3.204511e+07 | 3.207718e+07 | 3.212487e+07 | 3.217572e+07 | 3.223085e+07 | 3.228905e+07 | 3.234697e+07 | 3.239227e+07 | 3.242164e+07 | 3.247220e+07 | 3.251293e+07 | 3.255767e+07 | 3.260518e+07 | 3.265247e+07 | 3.268696e+07 | 3.270836e+07 | 3.274526e+07 | 3.277891e+07 | 3.281478e+07 | 3.285287e+07 | 3.289517e+07 | 3.292398e+07 | 3.294085e+07 | 3.296951e+07 | 3.299733e+07 | 3.302662e+07 | 3.305676e+07 | 3.308511e+07 | 3.310488e+07 | 3.311774e+07 | 3.314366e+07 | 3.316642e+07 | 3.319047e+07 | 3.321800e+07 | 3.323996e+07 | 3.325194e+07 |
NAN = [(c, covid_data[c].isna().mean()*100) for c in covid_data]
NAN = pd.DataFrame(NAN, columns=["column_name", "percentage"])
NAN
| column_name | percentage | |
|---|---|---|
| 0 | SNo | 0.000000 |
| 1 | ObservationDate | 0.000000 |
| 2 | Province/State | 25.487144 |
| 3 | Country/Region | 0.000000 |
| 4 | Last Update | 0.000000 |
| 5 | Confirmed | 0.000000 |
| 6 | Deaths | 0.000000 |
| 7 | Recovered | 0.000000 |
34 % of Province/State are unknown we will fill nan values with Unknown. 0
covid_data["Province/State"]= covid_data["Province/State"].fillna('Unknown')
Change Data Type for "Confirmed","Deaths" and "Recovered" columns to int
covid_data[["Confirmed","Deaths","Recovered"]] =covid_data[["Confirmed","Deaths","Recovered"]].astype(int)
Replacing "Mainland China" with "China"
covid_data['Country/Region'] = covid_data['Country/Region'].replace('Mainland China', 'China')
covid_data['Active_case'] = covid_data['Confirmed'] - covid_data['Deaths'] - covid_data['Recovered']
covid_data.head()
| SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 01/22/2020 | Anhui | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 1 | 2 | 01/22/2020 | Beijing | China | 1/22/2020 17:00 | 14 | 0 | 0 | 14 |
| 2 | 3 | 01/22/2020 | Chongqing | China | 1/22/2020 17:00 | 6 | 0 | 0 | 6 |
| 3 | 4 | 01/22/2020 | Fujian | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 4 | 5 | 01/22/2020 | Gansu | China | 1/22/2020 17:00 | 0 | 0 | 0 | 0 |
Data = covid_data[covid_data['ObservationDate'] == max(covid_data['ObservationDate'])].reset_index()
base_stats = pd.DataFrame(columns=['Dates','Confirmed','Deaths','Recovered','Active'])
base_stats['Dates'] = covid_confirmed.columns[4:]
base_stats['Confirmed'] = base_stats['Dates'].apply(lambda x: covid_confirmed[x].sum())
base_stats['Deaths'] = base_stats['Dates'].apply(lambda x: covid_deaths[x].sum())
base_stats['Recovered'] = base_stats['Dates'].apply(lambda x: covid_recovered[x].sum())
base_stats.reset_index(drop=False, inplace=True)
base_stats['Active'] = base_stats['index'].apply(lambda x: (base_stats['Confirmed'][x]-(base_stats['Deaths'][x]+base_stats['Recovered'][x])))
base_stats.head()
| index | Dates | Confirmed | Deaths | Recovered | Active | |
|---|---|---|---|---|---|---|
| 0 | 0 | 1/22/20 | 557 | 17 | 30 | 510 |
| 1 | 1 | 1/23/20 | 655 | 18 | 32 | 605 |
| 2 | 2 | 1/24/20 | 941 | 26 | 39 | 876 |
| 3 | 3 | 1/25/20 | 1433 | 42 | 42 | 1349 |
| 4 | 4 | 1/26/20 | 2118 | 56 | 56 | 2006 |
latest_stats_fig = go.Figure()
latest_stats_fig.add_trace(go.Treemap(labels = ['Confirmed','Active','Recovered','Deaths'],
parents = ['','Confirmed','Confirmed','Confirmed'],
values = [base_stats['Confirmed'].sum(), base_stats['Active'].sum(), base_stats['Recovered'].sum(), base_stats['Deaths'].sum()],
branchvalues="total", marker_colors = ['#118ab2','#ef476f','#06d6a0','#073b4c'],
textinfo = "label+text+value",
outsidetextfont = {"size": 30, "color": "darkblue"},
marker = {"line": {"width": 2}},
pathbar = {"visible": False}
))
latest_stats_fig.update_layout(#width=1000,
height=300)
latest_stats_fig.show()
base_stats_fig = go.Figure()
for column in base_stats.columns.to_list()[2:6]:
color_dict = {
"Confirmed": "#118ab2",
"Active": "#ef476f",
"Recovered": "#06d6a0",
"Deaths": "#073b4c"
}
base_stats_fig.add_trace(
go.Scatter(
x = base_stats['Dates'],
y = base_stats[column],
name = column,
line = dict(color=color_dict[column]),
hovertemplate ='<br><b>Date</b>: %{x}'+'<br><i>Count</i>:'+'%{y}',
)
)
for column in base_stats.columns.to_list()[2:6]:
color_dict = {
"Confirmed": "#149ECC",
"Active": "#F47C98",
"Recovered": "#24F9C1",
"Deaths": "#0C6583"
}
base_stats_fig.add_trace(
go.Scatter(
x = base_stats['Dates'],
y = base_stats['index'].apply(lambda x: (base_stats[column][x-7:x].sum())/7 if x>7 else (base_stats[column][0:x].sum())/7),
name = column+" 7-day Moving Avg.",
line = dict(dash="dash", color=color_dict[column]), showlegend=False,
hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day moving avg.</i>: %{y}'
)
)
base_stats_fig.update_layout(
updatemenus=[
dict(
buttons=list(
[dict(label = 'All Cases',
method = 'update',
args = [{'visible': [True, True, True, True, True, True, True, True]},
{'title': 'All Cases',
'showlegend':True}]),
dict(label = 'Confirmed',
method = 'update',
args = [{'visible': [True, False, False, False, True, False, False, False]},
{'title': 'Confirmed',
'showlegend':True}]),
dict(label = 'Active',
method = 'update',
args = [{'visible': [False, False, False, True, False, False, False, True]},
{'title': 'Active',
'showlegend':True}]),
dict(label = 'Recovered',
method = 'update',
args = [{'visible': [False, False, True, False, False, False, True, False]},
{'title': 'Recovered',
'showlegend':True}]),
dict(label = 'Deaths',
method = 'update',
args = [{'visible': [False, True, False, False, False, True, False, False]},
{'title': 'Deaths',
'showlegend':True}]),
]),
type = "dropdown",
direction="down",
# pad={"r": 10, "t": 40},
showactive=True,
x=0,
xanchor="left",
y=1.25,
yanchor="top"
),
dict(
buttons=list(
[dict(label = 'Linear Scale',
method = 'relayout',
args = [{'yaxis': {'type': 'linear'}},
{'title': 'All Cases',
'showlegend':True}]),
dict(label = 'Log Scale',
method = 'relayout',
args = [{'yaxis': {'type': 'log'}},
{'title': 'Confirmed',
'showlegend':True}]),
]),
type = "dropdown",
direction="down",
# pad={"r": 10, "t": 10},
showactive=True,
x=0,
xanchor="left",
y=1.36,
yanchor="top"
)
])
base_stats_fig.update_xaxes(showticklabels=False)
base_stats_fig.update_layout(
#height=600, width=600,
title_text="Basic Statistics for Covid19", title_x=0.5, title_font_size=20,
legend=dict(orientation='h',yanchor='top',y=1.15,xanchor='right',x=1), paper_bgcolor="mintcream",
xaxis_title="Date", yaxis_title="Number of Cases")
base_stats_fig.show()
covid_data.head()
| SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 01/22/2020 | Anhui | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 1 | 2 | 01/22/2020 | Beijing | China | 1/22/2020 17:00 | 14 | 0 | 0 | 14 |
| 2 | 3 | 01/22/2020 | Chongqing | China | 1/22/2020 17:00 | 6 | 0 | 0 | 6 |
| 3 | 4 | 01/22/2020 | Fujian | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 4 | 5 | 01/22/2020 | Gansu | China | 1/22/2020 17:00 | 0 | 0 | 0 | 0 |
daily_case_fig = make_subplots(rows=2, cols=2, vertical_spacing=0.05, horizontal_spacing=0.04, # shared_yaxes=True,
subplot_titles=('Confirmed','Active','Recovered','Deaths'),
x_title='Dates', y_title='# of Cases',)
daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Confirmed'][x]-base_stats['Confirmed'][x-1:x].sum()),
name='Confirmed',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Confirmed Count</i>: %{y}',
marker=dict(color='#118ab2')),row=1, col=1)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Confirmed'][x-7:x].sum()-base_stats['Confirmed'][x-8:x-1].sum())/7 if x>0 else 0),
name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
line=dict(dash="dash", color='#149ECC')),row=1, col=1)
daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Active'][x]-base_stats['Active'][x-1:x].sum()),
name='Active',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Active Count</i>: %{y}',
marker=dict(color='#ef476f')),row=1, col=2)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Active'][x-7:x].sum()-base_stats['Active'][x-8:x-1].sum())/7 if x>0 else 0),
name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
line=dict(dash="dash", color='#F47C98')),row=1, col=2)
daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Recovered'][x]-base_stats['Recovered'][x-1:x].sum()),
name='Recovered',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Recovered Count</i>: %{y}',
marker=dict(color='#06d6a0')),row=2, col=1)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Recovered'][x-7:x].sum()-base_stats['Recovered'][x-8:x-1].sum())/7 if x>0 else 0),
name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
line=dict(dash="dash", color='#24F9C1')),row=2, col=1)
daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Deaths'][x]-base_stats['Deaths'][x-1:x].sum()),
name='Deaths',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Death Count</i>: %{y}',
marker=dict(color='#073b4c')),row=2, col=2)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Deaths'][x-7:x].sum()-base_stats['Deaths'][x-8:x-1].sum())/7 if x>0 else 0),
name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', line=dict(dash="dash", color='#0C6583')),row=2, col=2)
daily_case_fig.update_xaxes(showticklabels=False)
daily_case_fig.update_layout(
#height=600, width=1100,
title_text="Daily change in cases of Covid19", title_x=0.5, title_font_size=20,
legend=dict(orientation='h',yanchor='top',y=1.1,xanchor='right',x=1), paper_bgcolor="mintcream")
daily_case_fig.show()
Confirmed cases in each Country
Data_per_country = covid_data.groupby(["Country/Region"])["Confirmed","Active_case","Recovered","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'
fig = go.Figure(data=[go.Table(
header=dict(
values=['<b>Country</b>','<b>Confirmed Cases</b>'],
line_color='darkslategray',
fill_color=headerColor,
align=['left','center'],
font=dict(color='white', size=12)
),
cells=dict(
values=[
Data_per_country['Country/Region'],
Data_per_country['Confirmed'],
],
line_color='darkslategray',
# 2-D list of colors for alternating rows
fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
align = ['left', 'center'],
font = dict(color = 'darkslategray', size = 11)
))
])
fig.update_layout(
title='Confirmed Cases In Each Country',
)
fig.show()
data_per_country = covid_data.groupby(["Country/Region","ObservationDate"])[["Confirmed","Active_case","Recovered","Deaths"]].sum().reset_index().sort_values("ObservationDate",ascending=True).reset_index(drop=True)
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
color=data_per_country['Active_case'],locationmode='country names',
hover_name=data_per_country['Country/Region'],
color_continuous_scale=px.colors.sequential.Tealgrn,
animation_frame="ObservationDate")
fig.update_layout(
title='Evolution of active cases In Each Country',
template='plotly_dark'
)
fig.show()
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
color=data_per_country['Recovered'],locationmode='country names',
hover_name=data_per_country['Country/Region'],
color_continuous_scale=px.colors.sequential.deep,
animation_frame="ObservationDate")
fig.update_layout(
title='Evolution of recovered cases In Each Country',
)
fig.show()
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
color=data_per_country['Deaths'],locationmode='country names',
hover_name=data_per_country['Country/Region'],
color_continuous_scale=px.colors.sequential.Tealgrn,
animation_frame="ObservationDate")
fig.update_layout(
title='Evolution of deaths In Each Country',
template='plotly_dark'
)
fig.show()
fig = go.Figure(data=[go.Bar(
x=Data_per_country['Country/Region'][0:10], y=Data_per_country['Confirmed'][0:10],
text=Data_per_country['Confirmed'][0:10],
textposition='auto',
marker_color='pink',
)])
fig.update_layout(
title='Most 10 infected Countries',
xaxis_title="Countries",
yaxis_title="Confirmed Cases",
template='plotly_white'
)
fig.show()
Recovered_per_country = covid_data.groupby(["Country/Region"])["Recovered"].sum().reset_index().sort_values("Recovered",ascending=False).reset_index(drop=True)
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'
fig = go.Figure(data=[go.Table(
header=dict(
values=['<b>Country</b>','<b>Recovered Cases</b>'],
line_color='darkslategray',
fill_color=headerColor,
align=['left','center'],
font=dict(color='white', size=12)
),
cells=dict(
values=[
Recovered_per_country['Country/Region'],
Recovered_per_country['Recovered'],
],
line_color='darkslategray',
# 2-D list of colors for alternating rows
fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
align = ['left', 'center'],
font = dict(color = 'darkslategray', size = 11)
))
])
fig.update_layout(
title='Recovered Cases In Each Country',
)
fig.show()
fig = px.pie(Recovered_per_country, values=Recovered_per_country['Recovered'], names=Recovered_per_country['Country/Region'],
title='Recovered cases',
)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
template='plotly_white'
)
fig.show()
fig = go.Figure(data=[go.Bar(
x=Recovered_per_country['Country/Region'][0:10], y=Recovered_per_country['Recovered'][0:10],
text=Recovered_per_country['Recovered'][0:10],
textposition='auto',
marker_color='lavender',
)])
fig.update_layout(
title='Most 10 infected Countries',
xaxis_title="Countries",
yaxis_title="Recovered Cases",
template='plotly_white'
)
fig.show()
Active_per_country = covid_data.groupby(["Country/Region"])["Active_case"].sum().reset_index().sort_values("Active_case",ascending=False).reset_index(drop=True)
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'
fig = go.Figure(data=[go.Table(
header=dict(
values=['<b>Country</b>','<b>Active Cases</b>'],
line_color='darkslategray',
fill_color=headerColor,
align=['left','center'],
font=dict(color='white', size=12)
),
cells=dict(
values=[
Active_per_country['Country/Region'],
Active_per_country['Active_case'],
],
line_color='darkslategray',
# 2-D list of colors for alternating rows
fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
align = ['left', 'center'],
font = dict(color = 'darkslategray', size = 11)
))
])
fig.update_layout(
title='Active Cases In Each Country',
)
fig.show()
fig = go.Figure(data=[go.Bar(
x=Active_per_country['Country/Region'][0:10], y=Active_per_country['Active_case'][0:10],
text=Active_per_country['Active_case'][0:10],
)])
fig.update_layout(
title='Most 10 infected Countries',
xaxis_title="Countries",
yaxis_title="Active Cases",
template='plotly_white'
)
fig.show()
Deaths_per_country = covid_data.groupby(["Country/Region"])["Deaths"].sum().reset_index().sort_values("Deaths",ascending=False).reset_index(drop=True)
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'
fig = go.Figure(data=[go.Table(
header=dict(
values=['<b>Country</b>','<b>Deaths</b>'],
line_color='darkslategray',
fill_color=headerColor,
align=['left','center'],
font=dict(color='white', size=12)
),
cells=dict(
values=[
Deaths_per_country['Country/Region'],
Deaths_per_country['Deaths'],
],
line_color='darkslategray',
# 2-D list of colors for alternating rows
fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
align = ['left', 'center'],
font = dict(color = 'darkslategray', size = 11)
))
])
fig.update_layout(
title='Deaths In Each Country',
)
fig.show()
fig = go.Figure(data=[go.Bar(
x=Deaths_per_country['Country/Region'][0:10], y=Deaths_per_country['Deaths'][0:10],
text=Deaths_per_country['Deaths'][0:10],
textposition='auto',
marker_color=' green'
)])
fig.update_layout(
title='Most 10 infected Countries',
xaxis_title="Countries",
yaxis_title="Deaths",
template='plotly_white'
)
fig.show()
base_stats_inc_df = pd.DataFrame(columns=['Index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'Daily Inc.'])
base_stats_inc_df[['Index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active']] = base_stats[['index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active']]
base_stats_inc_df['Daily Inc.'] = base_stats['index'].apply(lambda x: base_stats['Confirmed'][x]-base_stats['Confirmed'][x-1:x].sum())
days = np.array(base_stats_inc_df[['Index']]).reshape(-1, 1)
days_ex = []
for i in range(len(days)+30):
days_ex = days_ex+[[i]]
prediction_df = pd.DataFrame(columns=['Index', 'Confirmed Pred', 'Deaths Pred', 'Recovered Pred', 'Active Pred', 'Daily Inc. Pred'])
prediction_df['Index'] = list(flatten(days_ex))
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
from sklearn.metrics import r2_score
for col in base_stats_inc_df.columns[2:]:
count = np.array(base_stats_inc_df[[col]]).reshape(-1, 1)
X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(
days[50:], count[50:],
test_size=0.05, shuffle=False)
MAE, RSE, R2 = [], [], []
for j in range(1,10):
#creating the model
poly = PolynomialFeatures(degree=j)
train_x_poly = poly.fit_transform(X_train_confirmed)
regr_poly = linear_model.LinearRegression()
regr_poly.fit(train_x_poly, y_train_confirmed)
y_pred_poly = regr_poly.predict(poly.fit_transform(X_test_confirmed))
MAE.append(np.mean(np.absolute(y_pred_poly - y_test_confirmed)))
RSE.append(np.mean((y_pred_poly - list(flatten(y_test_confirmed))) ** 2))
R2.append(r2_score(y_pred_poly, list(flatten(y_test_confirmed))))
deg = RSE.index(min(RSE))+1
#print("best deg for column {} is {}".format(col, deg))
poly = PolynomialFeatures(degree=deg)
train_x_poly = poly.fit_transform(X_train_confirmed)
regr_poly = linear_model.LinearRegression()
regr_poly.fit(train_x_poly, y_train_confirmed)
col_name = col+' Pred'
prediction_df[col_name] = list(flatten(regr_poly.predict(poly.fit_transform(days_ex))))
prediction_fig = go.Figure()
pred_dict = {
"Confirmed": ["#118ab2", 'Confirmed', 'Predicted Confirmed','#149ECC'],
"Active": ["#ef476f", 'Deaths', 'Predicted Deaths', '#F47C98'],
"Recovered": ["#06d6a0", 'Recovered', 'Predicted Recovered','#24F9C1'],
"Deaths": ["#073b4c", 'Active', 'Predicted Active','#0C6583'],
"Daily Inc.": ["black", 'Daily Inc.', 'Predicted Daily Inc.','grey']
}
for z in base_stats_inc_df.columns[2:]:
z_pred = z+' Pred'
prediction_fig.add_trace(go.Scatter(x=list(flatten(days)), y=base_stats_inc_df[z],
line=dict(color=pred_dict[z][0]), name = pred_dict[z][1],
hovertemplate ='<br><b>Day number</b>: %{x}'+'<br><i>No.of cases </i>:'+'%{y}'))
prediction_fig.add_trace(go.Scatter(x=list(flatten(days_ex))[50:], y=prediction_df[z_pred][50:],
line=dict(dash="dash", color='black'), visible=False, name = pred_dict[z][2],
hovertemplate ='<br><b>Day number</b>: %{x}'+'<br><i>Predicted no.of cases </i>:'+'%{y}'))
prediction_fig.update_layout(
updatemenus=[
dict(
buttons=list(
[dict(label = 'Confirmed',
method = 'update',
args = [{'visible': [True, True, False, False, False, False, False, False, False, False]},
{'title': 'Confirmed Cases',
'showlegend':True}]),
dict(label = 'Deaths',
method = 'update',
args = [{'visible': [False, False, True, True, False, False, False, False, False, False]},
{'title': 'Deaths Cases',
'showlegend':True}]),
dict(label = 'Recovered',
method = 'update',
args = [{'visible': [False, False, False, False, True, True, False, False, False, False]},
{'title': 'Recovered Cases',
'showlegend':True}]),
dict(label = 'Active',
method = 'update',
args = [{'visible': [False, False, False, False, False, False, True, True, False, False]},
{'title': 'Active Cases',
'showlegend':True}]),
dict(label = 'Daily Inc.',
method = 'update',
args = [{'visible': [False, False, False, False, False, False, False, False, True, True]},
{'title': 'Daily Inc. Cases',
'showlegend':True}]),
]),
type = "buttons",
direction="down",
# pad={"r": 10, "t": 40},
showactive=True,
# x=1.01,
# xanchor="left",
y=1.1,
yanchor="top"
)
])
prediction_fig.update_xaxes(showticklabels=False)
prediction_fig.update_layout(
#height=500, width=1100,
title_text="Prediction for Covid19 Cases", title_x=0.5, title_font_size=20,
legend=dict(orientation='h',yanchor='top',y=1.12,xanchor='right',x=1), paper_bgcolor="mintcream",
xaxis_title="Number of Days <br> (Click on the buttons at the left to see the predictions)", yaxis_title="Count")
prediction_fig.show()
Data_india = covid_data [(covid_data['Country/Region'] == 'India') ].reset_index(drop=True)
Data_india.head()
| SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 447 | 01/30/2020 | Unknown | India | 1/30/20 16:00 | 1 | 0 | 0 | 1 |
| 1 | 510 | 01/31/2020 | Unknown | India | 1/31/2020 23:59 | 1 | 0 | 0 | 1 |
| 2 | 568 | 02/01/2020 | Unknown | India | 1/31/2020 8:15 | 1 | 0 | 0 | 1 |
| 3 | 630 | 02/02/2020 | Unknown | India | 2020-02-02T06:03:08 | 2 | 0 | 0 | 2 |
| 4 | 697 | 02/03/2020 | Unknown | India | 2020-02-03T21:43:02 | 3 | 0 | 0 | 3 |
fig = go.Figure(go.Bar(
x=Data_india['ObservationDate'],
y=Data_india['Active_case'],
marker_color='rgb(13,48,100)'
))
fig.update_layout(
title='Active cases In Each Day',
template='plotly_white',
xaxis_title="Active cases",
yaxis_title="Days",
)
fig.show()
fig = go.Figure(go.Bar(
x=Data_india['ObservationDate'],
y=Data_india['Active_case'],
marker_color='rgb(13,48,100)'
))
fig.update_layout(
title='Active cases In Each Day',
template='plotly_white',
xaxis_title="Active cases",
yaxis_title="Days",
)
fig.show()
fig = go.Figure(go.Bar(
x=Data_india['ObservationDate'],
y=Data_india['Recovered'],
marker_color='rgb(13,48,100)'
))
fig.update_layout(
title='Recovered cases In Each Day',
template='plotly_white',
xaxis_title="Recovered cases",
yaxis_title="Days",
)
fig.show()
fig = go.Figure(go.Bar(
x=Data_india['ObservationDate'],
y=Data_india['Deaths'],
marker_color='rgb(13,48,100)'
))
fig.update_layout(
title='Deaths In Each Day',
template='plotly_white',
xaxis_title="Deaths",
yaxis_title="Days",
)
fig.show()
Data_india_last = Data_india[Data_india['ObservationDate'] == max(Data_india['ObservationDate'])].reset_index()
Data_india_last
| index | SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7632 | 191894 | 12/31/2020 | Andaman and Nicobar Islands | India | 2021-04-02 15:13:53 | 4941 | 62 | 4820 | 59 |
| 1 | 7633 | 191895 | 12/31/2020 | Andhra Pradesh | India | 2021-04-02 15:13:53 | 881948 | 7104 | 871588 | 3256 |
| 2 | 7634 | 191912 | 12/31/2020 | Arunachal Pradesh | India | 2021-04-02 15:13:53 | 16711 | 56 | 16549 | 106 |
| 3 | 7635 | 191913 | 12/31/2020 | Assam | India | 2021-04-02 15:13:53 | 216139 | 1043 | 211838 | 3258 |
| 4 | 7636 | 191935 | 12/31/2020 | Bihar | India | 2021-04-02 15:13:53 | 251348 | 1393 | 245156 | 4799 |
| 5 | 7637 | 191969 | 12/31/2020 | Chandigarh | India | 2021-04-02 15:13:53 | 19682 | 316 | 18967 | 399 |
| 6 | 7638 | 191976 | 12/31/2020 | Chhattisgarh | India | 2021-04-02 15:13:53 | 278540 | 3350 | 263251 | 11939 |
| 7 | 7639 | 191995 | 12/31/2020 | Dadra and Nagar Haveli and Daman and Diu | India | 2021-04-02 15:13:53 | 3375 | 2 | 3364 | 9 |
| 8 | 7640 | 191999 | 12/31/2020 | Delhi | India | 2021-04-02 15:13:53 | 624795 | 10523 | 608434 | 5838 |
| 9 | 7641 | 192035 | 12/31/2020 | Goa | India | 2021-04-02 15:13:53 | 50981 | 737 | 49313 | 931 |
| 10 | 7642 | 192051 | 12/31/2020 | Gujarat | India | 2021-04-02 15:13:53 | 244258 | 4302 | 229977 | 9979 |
| 11 | 7643 | 192057 | 12/31/2020 | Haryana | India | 2021-04-02 15:13:53 | 262054 | 2899 | 255356 | 3799 |
| 12 | 7644 | 192064 | 12/31/2020 | Himachal Pradesh | India | 2021-04-02 15:13:53 | 55114 | 931 | 51387 | 2796 |
| 13 | 7645 | 192090 | 12/31/2020 | Jammu and Kashmir | India | 2021-04-02 15:13:53 | 120744 | 1880 | 115830 | 3034 |
| 14 | 7646 | 192093 | 12/31/2020 | Jharkhand | India | 2021-04-02 15:13:53 | 114873 | 1027 | 112206 | 1640 |
| 15 | 7647 | 192111 | 12/31/2020 | Karnataka | India | 2021-04-02 15:13:53 | 918544 | 12081 | 894834 | 11629 |
| 16 | 7648 | 192114 | 12/31/2020 | Kerala | India | 2021-04-02 15:13:53 | 755718 | 3042 | 687104 | 65572 |
| 17 | 7649 | 192139 | 12/31/2020 | Ladakh | India | 2021-04-02 15:13:53 | 9447 | 127 | 9132 | 188 |
| 18 | 7650 | 192140 | 12/31/2020 | Lakshadweep | India | 2021-04-02 15:13:53 | 0 | 0 | 0 | 0 |
| 19 | 7651 | 192160 | 12/31/2020 | Madhya Pradesh | India | 2021-04-02 15:13:53 | 240947 | 3595 | 227965 | 9387 |
| 20 | 7652 | 192166 | 12/31/2020 | Maharashtra | India | 2021-04-02 15:13:53 | 1928603 | 49463 | 1824934 | 54206 |
| 21 | 7653 | 192168 | 12/31/2020 | Manipur | India | 2021-04-02 15:13:53 | 28137 | 354 | 26601 | 1182 |
| 22 | 7654 | 192181 | 12/31/2020 | Meghalaya | India | 2021-04-02 15:13:53 | 13408 | 139 | 13085 | 184 |
| 23 | 7655 | 192195 | 12/31/2020 | Mizoram | India | 2021-04-02 15:13:53 | 4204 | 8 | 4091 | 105 |
| 24 | 7656 | 192207 | 12/31/2020 | Nagaland | India | 2021-04-02 15:13:53 | 11921 | 79 | 11624 | 218 |
| 25 | 7657 | 192251 | 12/31/2020 | Odisha | India | 2021-04-02 15:13:53 | 329306 | 1871 | 325103 | 2332 |
| 26 | 7658 | 192285 | 12/31/2020 | Puducherry | India | 2021-04-02 15:13:53 | 38096 | 633 | 37100 | 363 |
| 27 | 7659 | 192289 | 12/31/2020 | Punjab | India | 2021-04-02 15:13:53 | 166239 | 5331 | 157043 | 3865 |
| 28 | 7660 | 192299 | 12/31/2020 | Rajasthan | India | 2021-04-02 15:13:53 | 307554 | 2689 | 295030 | 9835 |
| 29 | 7661 | 192348 | 12/31/2020 | Sikkim | India | 2021-04-02 15:13:53 | 5877 | 127 | 5218 | 532 |
| 30 | 7662 | 192369 | 12/31/2020 | Tamil Nadu | India | 2021-04-02 15:13:53 | 817077 | 12109 | 796353 | 8615 |
| 31 | 7663 | 192373 | 12/31/2020 | Telangana | India | 2021-04-02 15:13:53 | 286354 | 1541 | 278839 | 5974 |
| 32 | 7664 | 192390 | 12/31/2020 | Tripura | India | 2021-04-02 15:13:53 | 33264 | 385 | 32751 | 128 |
| 33 | 7665 | 192405 | 12/31/2020 | Unknown | India | 2021-04-02 15:13:53 | 0 | 0 | 0 | 0 |
| 34 | 7666 | 192416 | 12/31/2020 | Uttar Pradesh | India | 2021-04-02 15:13:53 | 584966 | 8352 | 562459 | 14155 |
| 35 | 7667 | 192417 | 12/31/2020 | Uttarakhand | India | 2021-04-02 15:13:53 | 90616 | 1504 | 84149 | 4963 |
| 36 | 7668 | 192444 | 12/31/2020 | West Bengal | India | 2021-04-02 15:13:53 | 550893 | 9683 | 528829 | 12381 |
colors = ['rgb(2,58,88)','rgb(65,171,93)', 'rgb(127,0,0)']
labels = ["Active cases","Recovered","Deaths"]
values = Data_india_last.loc[0, ["Active_case","Recovered","Deaths"]]
fig = go.Figure(data=[go.Pie(labels=labels,
values=values)])
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,
marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.show()
Data_China = covid_data [(covid_data['Country/Region'] == 'China') ].reset_index(drop=True)
Data_China.head()
| SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 01/22/2020 | Anhui | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 1 | 2 | 01/22/2020 | Beijing | China | 1/22/2020 17:00 | 14 | 0 | 0 | 14 |
| 2 | 3 | 01/22/2020 | Chongqing | China | 1/22/2020 17:00 | 6 | 0 | 0 | 6 |
| 3 | 4 | 01/22/2020 | Fujian | China | 1/22/2020 17:00 | 1 | 0 | 0 | 1 |
| 4 | 5 | 01/22/2020 | Gansu | China | 1/22/2020 17:00 | 0 | 0 | 0 | 0 |
Data_china_last = Data_China[Data_China['ObservationDate'] == max(Data_China['ObservationDate'])].reset_index()
Data_china_last.head()
| index | SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | Active_case | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11006 | 191897 | 12/31/2020 | Anhui | China | 2021-04-02 15:13:53 | 993 | 6 | 986 | 1 |
| 1 | 11007 | 191931 | 12/31/2020 | Beijing | China | 2021-04-02 15:13:53 | 987 | 9 | 944 | 34 |
| 2 | 11008 | 191981 | 12/31/2020 | Chongqing | China | 2021-04-02 15:13:53 | 590 | 6 | 584 | 0 |
| 3 | 11009 | 192023 | 12/31/2020 | Fujian | China | 2021-04-02 15:13:53 | 513 | 1 | 488 | 24 |
| 4 | 11010 | 192028 | 12/31/2020 | Gansu | China | 2021-04-02 15:13:53 | 182 | 2 | 180 | 0 |
Data_china_per_state= Data_china_last.groupby(["Province/State"])["Confirmed","Active_case","Recovered","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
fig = px.pie(Data_china_per_state, values=Data_china_per_state['Confirmed'], names=Data_china_per_state['Province/State'],
title='Confirmed cases in China',
hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
fig = go.Figure(go.Bar(
x=Data_china_per_state['Active_case'],
y=Data_china_per_state['Province/State'],
orientation='h',
marker_color='#DC3912',))
fig.update_layout(
title='Active Cases In Each Province/State',
template='plotly_white',
xaxis_title="Active Cases",
yaxis_title="Province/State",
)
fig.show()
fig = go.Figure(go.Bar(
x=Data_china_per_state['Recovered'],
y=Data_china_per_state['Province/State'],
orientation='h',
marker_color='green',))
fig.update_layout(
title='Active Cases In Each Province/State',
template='plotly_white',
xaxis_title="Recovered Cases",
yaxis_title="Province/State",
)
fig.show()
fig = go.Figure(go.Bar(
x=Data_china_per_state['Deaths'],
y=Data_china_per_state['Province/State'],
orientation='h',
marker_color='black',))
fig.update_layout(
title='Deaths In Each Province/State',
template='plotly_white',
xaxis_title="Deaths",
yaxis_title="Province/State",
)
fig.show()
Data_china_total= Data_china_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
labels = ["Active cases","Recovered","Deaths"]
values = Data_china_total.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_china_total, values=values, names=labels, color_discrete_sequence=['green','royalblue','darkblue'], hole=0.5)
fig.update_layout(
title='Total cases in China : '+str(Data_china_total["Confirmed"][0]),
)
fig.show()
Data_US = covid_data [(covid_data['Country/Region'] == 'US') ].reset_index(drop=True)
Data_us_last = Data_US[Data_US['ObservationDate'] == max(Data_US['ObservationDate'])].reset_index()
Data_us_total= Data_us_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
labels = ["Active cases","Recovered","Deaths"]
values = Data_us_total.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_us_total, values=values, names=labels, color_discrete_sequence=['royalblue','darkblue','green'], hole=0.5)
fig.update_layout(
title='Total cases in United States : '+str(Data_us_total["Confirmed"][0]),
)
fig.show()
Data_us_per_state= Data_us_last.groupby(["Province/State"])["Confirmed","Active_case","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Confirmed'], names=Data_us_per_state['Province/State'],
title='Confirmed cases in United States',
hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Active_case'], names=Data_us_per_state['Province/State'],
title='Active cases in United States',
hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Deaths'], names=Data_us_per_state['Province/State'],
title='Deaths in United States',
hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
Data_Spain = covid_data [(covid_data['Country/Region'] == 'Spain') ].reset_index(drop=True)
Data_spain = Data_Spain[Data_Spain['ObservationDate'] == max(Data_Spain['ObservationDate'])].reset_index()
Data_spain_last= Data_spain.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
labels = ["Active cases","Recovered","Deaths"]
values = Data_spain_last.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_spain_last, values=values, names=labels, color_discrete_sequence=['royalblue','green','darkblue'], hole=0.5)
fig.update_layout(
title='Total cases in Spain : '+str(Data_spain_last["Confirmed"][0]),
)
fig.show()
Data_spain_per_state= Data_spain.groupby(["Province/State"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Confirmed'], height=700,
title='Confirmed cases in Spain', color_discrete_sequence = px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
fig.show()
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Recovered'], height=700,
title='Recovered cases in Spain', color_discrete_sequence = px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
fig.show()
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Active_case'], height=700,
title='Active cases in Spain', color_discrete_sequence = px.colors.sequential.deep)
fig.data[0].textinfo = 'label+text+value'
fig.show()
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Deaths'], height=700,
title='Deaths in Spain', color_discrete_sequence = px.colors.sequential.deep)
fig.data[0].textinfo = 'label+text+value'
fig.show()
Data_US_op= Data_US.groupby(["ObservationDate","Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
Data_Word = covid_data [(covid_data['Country/Region'] != 'US') ].reset_index(drop=True)
Data_WORD_last = Data_Word[Data_Word['ObservationDate'] == max(Data_Word['ObservationDate'])].reset_index()
Data_us_total= Data_us_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
Data_word_total= Data_WORD_last.groupby(["ObservationDate"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
Data_Word_op= Data_Word.groupby(["ObservationDate"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Confirmed'],
mode='lines',
name='Confirmed cases in US'))
fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Confirmed'],
mode='lines',
name='Confirmed cases in The Rest Of The Word'))
fig.update_layout(
title='Evolution of Confirmed cases over time in US and The Rest Of The Word',
template='plotly_white'
)
fig.show()
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Active_case'],
mode='lines',
name='Active cases in US'))
fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Active_case'],
mode='lines',
name='Active cases in The Rest Of The Word'))
fig.update_layout(
title='Evolution of Active cases over time in US and The Rest Of The Word',
template='plotly_dark'
)
fig.show()
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Recovered'],
mode='lines',
name='Recovered cases in US'))
fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Recovered'],
mode='lines',
name='Recovered cases in The Rest Of The Word'))
fig.update_layout(
title='Evolution of Recovered cases over time in US and The Rest Of The Word',
template='plotly_dark'
)
fig.show()
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Deaths'],
mode='lines',
name='Deaths in US'))
fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Deaths'],
mode='lines',
name='Deathsin The Rest Of The Word'))
fig.update_layout(
title='Evolution of Deaths over time in US and The Rest Of The Word',
template='plotly_white'
)
fig.show()